Determining associations and alignments of process elements and measurements in a process

ABSTRACT

Techniques for automatically determining, without user input, one or more sources of a variation in the behavior of a target process element operating to control a process in a process plant include using a process element alignment map to determine process elements upstream of the target process element in the process; performing a data analysis on data corresponding to the upstream elements with respect to the target element to determine behavior time offsets, strengths of impact, and impact delays; and determining the source(s) based on the data analysis outputs. Techniques may include automatically defining the process element alignment map by obtaining and processing data from a plurality of diagrams or data sources of the process and/or plant. Furthermore, the techniques may be performed during plant run-time by any high-volume, high density device such as centralized or embedded big data appliances, controllers, field or I/O devices, and/or by an unsupervised application.

CROSS REFERENCE TO RELATED APPLICATIONS

The present disclosure is related to U.S. patent application Ser. No.13/784,041, entitled “BIG DATA IN PROCESS CONTROL SYSTEMS” and filedMar. 3, 2013; U.S. patent application Ser. No. 14/174,413, entitled“COLLECTING AND DELIVERING DATA TO A BIG DATA MACHINE IN A PROCESSCONTROL SYSTEM” and filed Feb. 6, 2014; and U.S. patent application Ser.No. ______, filed concurrently herewith and entitled “DISTRIBUTED BIGDATA IN A PROCESS CONTROL SYSTEM” (Attorney Docket No. 06005/592952),the entire disclosures of each of which are hereby expresslyincorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to process plants and toprocess control systems, and more particularly, to determining processelement alignment in process plants and process control systems.

BACKGROUND

Distributed process control systems, like those used in chemical,petroleum, industrial or other process plants, typically include one ormore process controllers communicatively coupled to one or more fielddevices via analog, digital or combined analog/digital buses, or via awireless communication link or network. The field devices, which may be,for example, valves, valve positioners, switches and transmitters (e.g.,temperature, pressure, level and flow rate sensors), are located withinthe process environment and generally perform physical or processcontrol functions such as opening or closing valves, measuring processparameters, etc. to control one or more process executing within theprocess plant or system. Smart field devices, such as the field devicesconforming to the well-known Fieldbus protocol may also perform controlcalculations, alarming functions, and other control functions commonlyimplemented within the controller. The process controllers, which arealso typically located within the plant environment, receive signalsindicative of process measurements made by the field devices and/orother information pertaining to the field devices and execute acontroller application that runs, for example, different control moduleswhich make process control decisions, generate control signals based onthe received information and coordinate with the control modules orblocks being performed in the field devices, such as HART®,WirelessHART®, and FOUNDATION® Fieldbus field devices. The controlmodules in the controller send the control signals over thecommunication lines or links to the field devices to thereby control theoperation of at least a portion of the process plant or system. Forexample, the controllers and the field devices control at least aportion of a process being controlled by the process plant or system.

Information from the field devices and the controller is usually madeavailable over a data highway or communication network to one or moreother hardware devices, such as operator workstations, personalcomputers or computing devices, data historians, report generators,centralized databases, or other centralized administrative computingdevices that are typically placed in control rooms or other locationsaway from the harsher plant environment. Each of these hardware devicestypically is centralized across the process plant or across a portion ofthe process plant. These hardware devices run applications that may, forexample, enable an operator to perform functions with respect tocontrolling a process and/or operating the process plant, such aschanging settings of the process control routine, modifying theoperation of the control modules within the controllers or the fielddevices, viewing the current state of the process, viewing alarmsgenerated by field devices and controllers, simulating the operation ofthe process for the purpose of training personnel or testing the processcontrol software, keeping and updating a configuration database, etc.The data highway utilized by the hardware devices, controllers and fielddevices may include a wired communication path, a wireless communicationpath, or a combination of wired and wireless communication paths.

As an example, the DeltaV™ control system, sold by Emerson ProcessManagement, includes multiple applications stored within and executed bydifferent devices located at diverse places within a process plant. Aconfiguration application, which resides in one or more workstations orcomputing devices, enables users to create or change process controlmodules and download these process control modules via a data highway todedicated distributed controllers. Typically, these control modules aremade up of communicatively interconnected function blocks, which areobjects in an object oriented programming protocol that performfunctions within the control scheme based on inputs thereto and thatprovide outputs to other function blocks within the control scheme. Theconfiguration application may also allow a configuration designer tocreate or change operator interfaces which are used by a viewingapplication to display data to an operator and to enable the operator tochange settings, such as set points, within the process controlroutines. Each dedicated controller and, in some cases, one or morefield devices, stores and executes a respective controller applicationthat runs the control modules assigned and downloaded thereto toimplement actual process control functionality. The viewingapplications, which may be executed on one or more operator workstations(or on one or more remote computing devices in communicative connectionwith the operator workstations and the data highway), receive data fromthe controller application via the data highway and display this data toprocess control system designers, operators, or users using the userinterfaces, and may provide any of a number of different views, such asan operator's view, an engineer's view, a technician's view, etc. A datahistorian application is typically stored in and executed by a datahistorian device that collects and stores some or all of the dataprovided across the data highway while a configuration databaseapplication may run in a still further computer attached to the datahighway to store the current process control routine configuration anddata associated therewith. Alternatively, the configuration database maybe located in the same workstation as the configuration application.

In a process plant or process control system, when evidence of anabnormal condition or fault occurs (e.g., when an alarm is generated, orwhen a process measurement or actuator is found to have excessivevariation), an operator, instrument technician or process engineertypically uses an analytics tool in combination with his or herknowledge of the process being controlled by the system and its flowpath through the system to attempt to determine upstream measurementsand process variables that may have contributed to the production of theevidence of the abnormal condition or fault. For example, an operatormay use the DeltaV™ batch analytics product or another continuous dataanalytics tool to attempt to determine the contributions of variousprocess variables and/or measurements to an abnormal or fault condition.Typically, an operator or user identifies candidate upstream factors(e.g., measurements, process variables, etc.) based on his or herknowledge of the process and provides these candidates to the analyticstool. Subsequently, these data analytics tools utilize principalcomponent analysis (PCA) to determine which of the candidate upstreamfactors impact downstream predicted quality parameters. The processcontrol systems that are currently commercially available typically donot provide information on the flow path through the process andassociated measurements and actuators along this path, and instead relyon a human to input this information into analytics tools. Consequently,as the set of candidates that is input into the tool is filtered by aperson, the list of candidates may be incomplete and/or erroneous, andmay not be consistent from person to person.

Additionally, the architecture of currently known process control plantsand process control systems is strongly influenced by limited controllerand device memory, communications bandwidth and controller and deviceprocessor capability. For example, in currently known process controlsystem architectures, the use of dynamic and static non-volatile memoryin the controller is usually minimized or, at the least, managedcarefully. As a result, during system configuration (e.g., a priori), auser typically must choose which data in the controller is to bearchived or saved, the frequency at which it will be saved, and whetheror not compression is used, and the controller is accordingly configuredwith this limited set of data rules. Consequently, data which could beuseful in troubleshooting and process analysis is often not archived,and if it is collected, the useful information may have been lost due todata compression.

The limitations of currently known process plants and process controlsystems discussed above and other limitations may undesirably manifestthemselves in the operation and optimization of process plants orprocess control systems, for instance, during plant operations, troubleshooting, and/or predictive modeling. For example, such limitationsforce cumbersome and lengthy work flows that must be performed in orderto obtain data for troubleshooting and generating updated models, andeven then, the troubleshooting results and models may be incomplete ornot fully representative of the actual system, as the inputs to theirgeneration rely on a particular operator's experience and knowledge.

“Big data” generally refers to a collection of one or more data setsthat are so large or complex that traditional database management toolsand/or data processing applications (e.g., relational databases anddesktop statistic packages) are not able to manage the data sets withina tolerable amount of time. Typically, applications that use big dataare transactional and end-user directed or focused. For example, websearch engines, social media applications, marketing applications andretail applications may use and manipulate big data. Big data may besupported by a distributed database which allows the parallel processingcapability of modern multi-process, multi-core servers to be fullyutilized.

SUMMARY

Techniques, systems, apparatuses, and methods for automaticallyidentifying potential sources of faults, abnormal operations, and/orvariations in the behavior of process elements used to control a processin a process plant are disclosed. Generally, said techniques, systems,apparatuses, and methods automatically determine or define, without anyuser input, the alignment of process elements in a flow path of aprocess, e.g., a process element alignment map. The process elementsincluded in the map comprise a plurality of control devices, processvariables, measurements, and/or other process elements, each of whichhas an active role during run-time to control the process. Additionallyor alternatively, said techniques, systems and methods automaticallydetermine, without any user input and using the process elementalignment map, one or more process elements that contribute to faults,abnormal operations, and/or variations in the behaviors of other processelements.

For example, systems, apparatuses, and/or techniques that areparticularly configured to perform a method of determining sources offaults, abnormal operations, and/or variations in the behavior ofprocess elements that are controlling a process are disclosed. Themethod includes receiving (e.g., manually or automatically) anindication of a target process element that is included in the pluralityof process elements and at which faults, abnormal operations, and/orvariations in behavior are observed or detected. The method alsoincludes defining, based on the target process element and a pluralityof diagrams of the process or of the process plant, at least a portionof a process element alignment map of the process. Additionally, themethod includes determining, based on the process element alignment map,a set of process elements that are upstream, in the flow path of controlof the process, of the target process element, and providing indicationsof the upstream process elements to a data analysis (e.g., dataanalytics) to determine a respective strength of an impact of eachupstream process element on the behavior of the target process element.In particular, the set of inputs to the data analysis includes theindications of the upstream set of process elements and excludes anyuser-generated input. The method further includes determining, based onthe respective strengths of impacts of the upstream set of processelements, at least a subset of the upstream set of process elements tobe the one or more sources of a variation in the behavior of the targetprocess element, and causing an indication of the one or more sources ofthe variation in the behavior of the target process element to beprovided to a recipient application. The recipient application may be auser interface application or another application.

Systems, apparatuses, and/or techniques that are particularly configuredfor performing a method to automatically determine a process elementalignment map of a plurality of process elements controlling at least aportion of a process in a process plant are disclosed. The methodincludes obtaining or extracting a set of data from a plurality of datasources. The plurality of data sources stores data descriptive and/orindicative of the plurality of the process elements, and the obtained orextracted data includes, for each of the plurality of process elements,a respective identification of the each process element and anindication of a respective physical location of the each process elementin the process plant. The method further includes determining, based onthe obtained or extracted set of data, the process element alignment mapof the process, where the process element alignment map indicates, foreach process element, the respective identification of the each processelement and an indication of a respective order of the process elementin the process element alignment map.

Systems, apparatuses, and/or techniques that are particularly configuredfor performing a method to automatically determine one or more sourcesof a fault, an abnormal operation, and/or a variation of a behavior of atarget process element used in a process plant to control a process aredisclosed. The method includes receiving an indication of the targetprocess element (e.g., manually or automatically), and determining,using at least a portion of a process element alignment map, a subset ofthe plurality of process elements that are upstream of the targetprocess element, where each upstream process element has a respectiveorder in the process element alignment map that is ahead of or adjacentto the respective order of the target process element in the processelement alignment map. Additionally, the method includes causing the setof upstream process elements to be used in a data analysis to determinerespective impact delays for the upstream process elements, where arespective impact delay of a specific upstream process elementcorresponds to a time offset from a time at which a particular eventoccurs at the specific upstream process element to a time at which achange in the behavior of the target process element resulting from theoccurrence of the particular event at the specific upstream processelement occurs. The method further includes determining, based on therespective impact delays of the upstream process elements, at least aportion of the set of upstream process elements to be the one or moresources of the fault, abnormal operation, and/or variation of thebehavior of the target process element, and providing, identifying, orindicating the one or more sources to one or more recipientapplications.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example set of primary process elements used tocontrol an example process in a process plant or process control system;

FIGS. 2A-2C each illustrates a different example format for presentingat least a portion of a process element alignment map of a process;

FIG. 3 is a flow diagram of an example method for automaticallydetermining at least a part of a process element alignment map of aprocess;

FIGS. 4A-4D each illustrate a different example type of data source fromwhich a process element alignment map is able to be determined;

FIG. 5 is a block diagram of an example data flow used to generate aprocess element alignment map;

FIG. 6 is a flow diagram of an example method for determining sources ofvariations of process elements operating in a process plant to control aprocess;

FIG. 7 is an example of a user interface display that indicates a set ofsources of variations in the behavior of a particular process elementand their respective contributions;

FIG. 8 is a flow diagram of an example method for determining sources ofvariations of process elements operating in a process plant to control aprocess;

FIG. 9 is a block diagram of an example process control system in whichthe techniques disclosed herein may be implemented and included;

FIG. 10A is a block diagram of an example process control centralizedbig data appliance in a process control system or plant;

FIG. 10B is a block diagram of an example centralized big data device ina process control system or plant; and

FIG. 10C is a block diagram of an example distributed big data device ina process control system or plant.

DETAILED DESCRIPTION

Techniques, systems, and methods for automatically identifying potentialsources of variation in and/or abnormal operation of process elementsused to control a process in a process plant are disclosed herein.Generally, said techniques, systems and methods automatically determineor define, without any user input, a process flow path or processelement alignment map of a process. Additionally or alternatively, saidtechniques, systems and methods automatically determine, again withoutany user input and using the process alignment map, one or more processelements that are sources of variation of behaviors of other processelements, as is explained below.

In currently known process control plants and systems, when evidence ofan abnormal condition, variation or fault at a particular processelement (e.g., a process device, a process variable, or a processmeasurement) occurs, typically an operator, instrument technician, orprocess engineer uses an analytics tool in combination with his or herknowledge of the process being controlled by the system to attempt todetermine upstream measurements and process variables that may havecontributed to the production of the evidence of the abnormal condition,variation or fault. Upon being alerted to the abnormal condition (e.g.,by an alert or an alarm), an operator attempts to determine the cause ofthis condition by using an analytics tool and indications ofmeasurements and/or process variables that he or she feels may becontributing to this condition. The analytics tool performs an analysisto determine the relative effects of the input measurements and/orprocess variables on the abnormal behavior of the particular or targetprocess element, and the analytics tool provides information regardingthe relative effects of the operator-indicated process elements to theoperator so that he or she can further investigate the condition.

This approach may suffer from incompleteness, as well as from beingcumbersome and requiring a relatively large amount of time to execute.For example, this approach is dependent on the particular operator'sknowledge of the process, which may or may not be complete, thorough, oraccurate. Accordingly, the information generated by the analytics toolmay not reflect the actual source of the variation, and can be maximallyonly as accurate or correct as the data which was input by the operator.

On the other hand, the automatically determined process elementalignment map disclosed herein indicates all process elements (e.g., acomplete or comprehensive set of process variables, processmeasurements, process devices and/or other process elements) that havean active role during run-time to control the process during normaloperations, e.g., by taking a measurement, performing a physicalfunction or causing another element to perform a physical function,controlling a variable, providing a value to another process element tocontrol a variable, generating dynamic data, receiving and operating ondynamic data, and the like. This complete or comprehensive set ofprocess elements that have active roles in control of the process isgenerally referred to interchangeably herein as “primary sources” ofprocess control, or as “primary process elements” or “primary controlsources” of the process. For example, manipulated and controlledvariables (e.g., as identified by tags or other identifiers) of acontrol routine that has been instantiated in a particular controllerare primary sources of control of the process and as such, are indicatedin the process element alignment map, whereas theyet-to-be-instantiated, user-generated configuration of the controlroutine that is produced at a user workstation is not a primary sourceof control of the process, as the yet-to-be-instantiated version of theconfiguration does not directly operate to control the process duringrun-time in the process plant. In other examples, indications ofcontrolled variables, sensors, and valves are included in the processelement alignment map, whereas indications of operator display views,diagnostic equipment, and auxiliary piping are excluded from the processelement alignment map.

Typically, the process element map is automatically determined (e.g.,determined without using or requiring any user input), for example, byextracting or obtaining identifications, physical locations within theprocess plant, and, optionally, other descriptive information of processelements from multiple data sources. The extracted or obtained data maythen be automatically ordered to reflect the relative sequence ofactivation or active participation of the primary process elements tocontrol the process during run-time. Thus, for each primary processelement indicated in the process element alignment map, the map includesan indication of an order of participation or activation of that primaryprocess element (e.g., with respect to an order of participation oractivation of another primary process element) while the process isbeing controlled, and these relative orders or positions within theprocess element alignment map may be described or indicated accordingly.For example, a process element corresponding to a first valve that iscontrolled to release raw input materials into a tank for initialprocessing is ordered in the process element alignment map ahead of(e.g., is “upstream of”) a process element corresponding to a secondvalve that is controlled to release the final product or output of theprocess into a holding area to await packaging. Conversely, the secondvalve is ordered after or is “downstream” of the first valve within theprocess element alignment map of the process.

To illustrate the concept of a process element alignment map of aprocess being controlled by a process plant or process control system,and the ordered participation of process elements included therein tocontrol the process, FIG. 1 depicts a block diagram of primary processelements of an example process plant 100 that operates to control anexample process 102 for producing powdered laundry detergent. In FIG. 1,the process elements used in controlling the process 102 are indicatedby their respective tags, which represented in FIG. 1 using theconvention of a circle enclosing a three-digit loop number and two orthree leading letters that identify the process element's function. Sometags may also include one or more letters appended to the tag or loopnumber.

The example process 102 combines the raw materials linear alkyl benzenesulfonic acid 105 and sodium bicarbonate 108 in a reactor 110. Thereaction produces soap, and also produces water and carbon dioxide asby-products. The chemical reaction may be represented by the expression:

(S)OH+NaHCO₃→(S)ONa+H₂O+CO₂,

where (S) is the sulfonate group HSO₂. The carbon dioxide by-product isvented 112 from the reactor 110, and at least a portion 115 a of thereactor discharge (which includes both soap and water) is transferred toa surge tank 118 for temporary storage. The surge tank 118 may be sizedto serve as a buffer between different sections of the process plant100, one section of which performs the chemical reaction in the reactor110, and another section that processes the output of the chemicalreaction in spray dryer towers 120 a, 120 b.

In the latter section of the process plant 100, the reactor discharge135 stored in the surge tank 118 is sprayed 140 a, 140 b into the topsof one or both of the spray dryer towers 120 a, 120 b. As the dischargeparticles fall down the length of the towers 120 a, 120 b, the particlesare exposed to respective hot streams of air 122 a, 122 b that causemoisture to be removed from the particles. The drier particles settle atthe bottom of the spray dryer towers 120 a, 120 b and are separated,e.g., by using cyclone separators 123 a, 123 b. In the example processplant 100, any residual moisture remaining in the separated particles isdetected and/or quantified for quality control, safety, and/or processcontrol purposes. For example, residual moisture remaining in theparticles after separation 123 a, 123 b may be detected respectively byweight sensors WT315 and WT325, and/or by moisture sensors AT314 andAT324. The separated particles, e.g., the powdered laundry detergentend-product produced by the process 102, are collected and stored insilos 125 a, 125 b, from which the end-product will eventually removedfor packaging (not shown).

As shown in FIG. 1, at various points along the process elementalignment map of the process 102, controllers execute respectivealgorithms to control respective portions of the overall process 102.For example, with regard to portions of the process 102 corresponding tothe reactor 110, the loop 301 adjusts the flow rate 128 of linear alkylbenzene sulfonic acid 105 into the reactor 110. Specifically, in theloop 301, a controller FC301 (e.g., a flow controller) executes arespective control algorithm to control a valve FV301 to adjust theinput flow rate 128, and a measurement device or sensor FT301 determinesthe output flow rate from the pump connected to the acid source 105, andprovides an indication of the flow rate to the controller FC301 to useas an input to the control algorithm to determine the adjustment of thevalve FV301. Another loop 302 controls the amount of sodium bicarbonate130 provided to the reactor 110. Specifically, in the loop 302, acontroller FFC302 executes a respective control algorithm to control theinput amount of sodium bicarbonate based on the detected, measured, orsensed stream FT301 of the sulfonic acid and based on a detection,measurement, or sensing AT303 of any residual solids that are leftoverfrom the reaction, e.g., solids that were not utilized in the reaction.The occurrence of residual solids may be caused by, for example,differing concentrations across different stocks or lots of inputmaterials 105, 108.

With further regard to portions of the process 102 corresponding to thereactor 110, in the loop 304, a controller PC304 controls a valve PV304to vent 112 the by-product carbon dioxide from the reactor 110. Thecontroller PC304 adjusts the valve PV304 to vent more or less carbondioxide based on a pressure of the reactor 110, e.g., as detected bypressure sensor PT304. Additionally, in the loop 307, an amount or levelof reactants contained within the reactor 110 is controlled. Forinstance, a controller LC307 adjusts a valve LV307 based on a level oramount of reactants in the reactor 110, e.g., as measured or detected bysensor LT307, to change the flow rate of reactor discharge 132 (e.g.,the soap and the water produced by the chemical reaction) into the surgetank 118, thus changing the level of reactants contained within thereactor 110. For example, if the level of reactants measured by sensorLT307 rises above a pre-determined level, the controller LC307 mayadjust the valve LV307 to increase the flow of reactor discharge 132into the surge tank 118.

Further, the loops 305 and 306 allow the process 102 to monitor thetemperature of the reactor discharge 115 as compared with thetemperature of the reactants currently contained within the reactor 110.For example, the sensor TT305 measures the temperature of the reactantscurrently contained within the reactor, and the sensor TT306 measures atemperature of a portion 115 b of the reactor discharge 115. Based on acomparison of these temperatures, controller TC306 executes a respectivecontrol algorithm to control a valve TV306 to shunt away more or less ofthe reactor discharge 115 c for cooling purposes rather than returningthe discharge 115 b back to the reactor 110. In some cases (not shown),shunted reactor discharge 115 c that has been sufficiently cooled may bereturned to the reactor 110.

Turning now to the surge tank 118, in the loop 308, a sensor LT308measures a level of reactor discharge contained within the tank 118, andbased on the measured level, a controller LC308 controls a splitterLY308 to indicate a desired division of the output of the surge tankreactor discharge 135 across two spray dryer towers 120 a, 120 b, e.g.,by indicating respective splits (e.g., bias and/or gain) LY308A andLY308B to the respective controllers FC310 and FC320 (e.g., flowcontrollers) respectively controlling the streams of reactor dischargeprovided to the dryers 120 a, 120 b and included in the loop 308 (e.g.,streams 138 a and 138 b, respectively).

With specific regard to the spray dryer tower 120 a, in the loop 310,the controller FC310 adjusts the flow rate of the reactor dischargespray 140 a into the top of the tower 120 a by executing a respectivecontrol algorithm that controls a variable speed drive VSD1 connected toa pump 139 a. As discussed above, controller FC310 receives anindication of a desired or target portion of the surge tank reactordischarge 135 (e.g., as indicated by LY308A) that is to be provided tothe tower 120 a, and utilizes this target or setpoint LY308A incombination with an actual detected, measured, or sensed portion 138 aof the surge tank output 135 (e.g., as detected by sensor FT310) todetermine a suitable control signal to provide to the VSD1 forcontrolling the pump 139 a.

With further regard to control of portions of the process 102 thatcorrespond to the spray dryer tower 120 a, in the loop 313, a controllerTC313 executes a respective control algorithm to control an air heater142 a (e.g., a gas air heater or other suitable type of heater) to varythe temperature of the hot air stream 122 a into the tower 120 a. Asshown in FIG. 1, the control algorithm executed by controller TC313receives, as inputs, an indication of a quantity of detected moistureincluded in the end-product (e.g., a quantity or ratio of detectedmoisture as detected by sensor AT314) and an indication of a temperatureof an actual, real-time flow of hot air 122 a into the tower 120 a(e.g., as detected by sensor TT313). Based on the received inputs, thecontroller TC313 adjusts an amount of gas, fuel or other energy providedto the heater 142 a (e.g., by controlling valve TV313), thus controllingthe actual temperature of the hot air 122 a generated by the heater 142a and provided to the tower 120 a. In another loop 312, anothercontroller FC312 executes a respective control algorithm to control avariable speed drive VSD2 to adjust the volume of air 145 a provided tothe air heater 142 a. For example, based on a measured actual volume ofair generated by an air fan 148 a (e.g., as detected by sensor FT312)and based on a desired production rate of the powdered laundrydetergent, the controller FC312 determines an adjustment to control thevolume of air provided by the air fan 148 a to the heater 142 a, andprovides an indication of this adjustment to the variable speed driverVSD2. The desired production rate may be configured into the controlalgorithm executed by the controller FC312, or may be provided to thecontroller FC312 as an additional input, either manually orautomatically (not shown).

Occasionally, the spray dryer tower 120 a may become less than optimallyoperable. The process 102 detects and mitigates this undesirablesituation by using the loop 311 to sense a pressure of the spray tower120 a, e.g., by using pressure sensor PT311 and providing the sensedpressure as an input to a controller PC311. The controller PC311executes a respective control algorithm which will send a signal tooverride the control signal to the driver VSD1 that drives the pump 139a. For example, the controller PC311 may send an override signal to thecontrol selector FY310 when the sensed pressure reaches a particularthreshold (and in some cases, has been sustained at this threshold overa defined duration of time), or the controller PC311 may continuouslysend a signal indicative of the sensed pressure to the selector FY310.The control selector FY310 selects the VSD1 control signal generated bycontroller FC310 or the override signal generated by the controllerPC311, and provides an indication of the selected input to the variablespeed drive VSD1 to control the flow of reactor discharge 140 a providedto the spray dryer tower 120 a. For example, the control selector FY310may default to providing the flow control signal generated by controllerFC310 to the drive VSD1, and may switch to providing the override signalgenerated by the controller PC311 to the drive VSD1 when the overridesignal is generated, when the override signal is above or below acertain threshold, and/or when the override signal has been above orbelow the threshold for a predetermined amount of time. In somesituations, the control selector FY310 may cause an alarm or other alertto be generated. In some embodiments, the control selector FY310 maycause the splitter LY308, LY308A, LY308B to divert a higher (or lower)proportion of the surge tank output 135 to the other spray tower 120 b.

It is noted that while the above discussion focuses primarily on thespray dryer tower 120 a, the spray dryer tower 120 b includescontrollers, sensors, and other equipment and entities similar to thosediscussed for the spray dryer tower 120 a, and may operate in a similarmanner as the spray dryer tower 120 a. In FIG. 1, the equipment andother entities corresponding to spray dryer tower 120 b are denoted bythe same reference numbers as used for the spray dryer tower 120 a, butsaid reference numbers are appended with a “b” suffix instead of an “a”suffix.

In view of the above, the process element alignment map of the process102 as shown in FIG. 1 generally occurs, moves, flows, or is orderedfrom left to right, e.g., from obtaining the input raw materials fromsources 105, 108, to processing the raw materials in the reactor 110, toprocessing the reacted materials in the spray dryer towers 120 a, 120 b,to storing the end product 125 a, 125 b. Accordingly, terms thatindicate the relative orders of the process elements that activelyparticipate in controlling the process 102 in real-time are used herein.As an example, as used herein, the term “upstream process elements”refers to primary process elements that generally have an earlier,active participation in controlling the process in real-time, and theterm “downstream process elements” refers to primary process elementsthat generally have a later, active participation in controlling theprocess. For instance, combining the reactants 105, 108 in the reactor110 occurs upstream of drying the reactor discharge 135 in the spraydryer towers 120 a, 120 b. As such, the process elements participatingin controlling the release of reactants105, 108 into the reactor 110(e.g., process elements AT303, AC303, FFC302, FT301, FC301, FV301, andSC302) are upstream of (or ordered in front of or before) the processelements participating in controlling the rate of drying in a spraydryer tower (e.g., process elements TT313, AC314, TC313, TV313, FT312and FC312 for spray dryer tower 120 a). Storing the end product in thesilos 125 a, 125 b occurs downstream of splitting the surge tankdischarge 135. Thus, the process elements AT314 and WT315 are downstreamof (or ordered after) the process elements LT308, LC308 and LY308 in theprocess element alignment map.

In some cases, a relative order of a particular process element may beequivalent to the relative order of another particular process element.For example, the valve FV301 controlling the release of acid flow intothe reactor 110 is ordered, in the process alignment map, adjacent tothe speed/frequency process element SC302 that controls the release ofsodium bicarbonate into the reactor 110.

The process element alignment map may be represented, e.g., at a userinterface, in any suitable format. FIG. 2A illustrates, in an exampletable format 150, a portion of an example process element alignment maprepresentation corresponding to the portion 152 of the process 102indicated by the dashed area 152 in FIG. 1. In the embodiment shown inFIG. 2A, the flow or progression of the process 102 moves from top tobottom of the table 150, so that the process elements indicated near thetop of the table 150 generally are upstream of process elementsindicated near the bottom of the table 150. In other embodiments,though, the progression of the process 120 may be ordered in the tableformat 150 from bottom to top, from left to right, or in any otherdesired direction.

Specifically, the table format 150 includes an ordering or alignment ofprocess elements by area (reference 155 a), e.g., the area correspondingto the surge tank 118 is indicated as being upstream of the areacorresponding to the spray dryer tower 1 (reference 120 a). Within eacharea, the order or alignment of vessels and lines associated with thearea is indicated (reference 155 b) in the table 150. For example,within the area of the surge tank 118, the pipe 132 is shown as beingupstream of the tank 118 which, in turn, is upstream of the pipe 135.Additionally, for each vessel or line, an ordering or alignment ofprimary process elements that actively participate in controlling theprocess 102 is indicated (reference 155 c) in the table 150, e.g., byusing their respective tags or defined identifiers. For example, theflow through the pipe 132 is controlled by the loop 307 including theprocess elements LT307, LC307 and LV307.

The table 150 may optionally include or provide links or references toany additional information 155 d that is descriptive of a particulararea 155 a, vessel or line 155 b, and/or process element 155 c. Forexample, the other information 155 d may include, for any primaryprocess elements 155 c that are physical devices such as valves,sensors, and other field devices, an image of the device, a model and/orserial number, and/or an indication of an absolute physical location ofthe physical device within the process plant (e.g., by using globalpositioning satellite (GPS) coordinates or other suitable locationidentifier).

FIG. 2B illustrates, in an example graph or graphic format 160, aportion of an example process element alignment map representationcorresponding to the portion 152 of the process 102. In the embodimentshown in FIG. 2B, the flow or progression of the process 102 moves fromleft to right, so that the process elements indicated nearer to the leftof the graph 160 generally are upstream of process elements indicatednearer to the right of the graph 160. In other embodiments, though, theprogression of the process 120 may be ordered from right to left, frombottom to top, or in any other desired direction. In the graph format160, surge tank area 162 a, spray dryer tower area 1 (reference 162 b),and spray dryer tower area 2 (reference 162 c) are separated by dashedlines and respectively labeled. Vessels and equipment are represented bygraphical shapes, and lines or pipes are represented by directionalvectors or lines indicating the directional flow of materials (which, insome cases, may be intermediate materials) through the controlledprocess 102. Identifiers of process elements that respectively areassociated with or correspond to specific vessels and lines arepositioned proximate to the graphic of the specific vessel or line. Forexample, the graph 160 indicates that, in the surge tank area 162 a,intermediate materials flow through the pipe 132 to be received by thesurge tank 118, and this flow of the materials into the surge tank 118is controlled by the process elements LT307, LC307 and LV307 of the loop307. It is understood that although graphical shapes and arrows are usedas conventions in the format 160, any desired directed graph symbols andrepresentations may be additionally or alternatively utilized in graphor graphic formats 160.

Referring to FIG. 2C, in another example format 170, nested views of aprocess element alignment map representation that include active linksor user controls is provided on a user interface. A top or initial view170 indicates the alignment or flow of the portion 152 of the process102 through various areas of the process plant (e.g., from the surgetank 118 to the spray dryer tower 1 (reference 120 a) and to the spraydryer tower 2 (reference 120 b) using graphical shapes and arrows,although any desired convention may be additionally or alternativelyused to represent process elements and flows in the format 170, such asselectable text. In the example format 170, if a user selects thegraphic of the line 172 and/or the surge tank 118 graphic, a subsequentview that includes details of the selected items (and optionally,surrounding items) may be displayed. For example, upon selection of thegraphic of the line 172 and/or the surge tank 118 graphic of FIG. 2C,the area surge tank graphic 162 a of FIG. 2B including selectableelements thereon may be displayed. If the user then selects a particulargraphic or identifier included in the area surge tank graphic 162 a,still additional nested information may be displayed. For example, ifthe user selects the identifier LT307 on the area surge tank graphic 162a of FIG. 2B, a pop-up window including an image of the physical sensordevice and/or a GPS coordinate of the particular sensor within theprocess plant may be displayed. Additionally or alternatively, otherinformation associated with LT307 may be displayed.

The table format 150, the graph format 160, and the nested view format170 are only examples of possible formats in which at least portions ofthe process element alignment map may be presented, e.g., at a userinterface. Any desired or suitable format may be utilized forpresentation of some or all of the process element alignment map.

Furthermore, any desired or suitable format for storing the processelement alignment map may be utilized. For example, an indication of aparticular process element used for controlling the process 102 inreal-time may be stored in a database (or in some other suitable datastorage format) along with an indication of the relative order of theparticular process element in the process alignment map to one or moreother process elements used for controlling the process 102 inreal-time. In some scenarios, metadata corresponding to the particularprocess element is also stored. For example, an image of the particularprocess element, an indication of its physical location within theprocess plant, and/or a link to these and/or other identifying ordescriptive information of the particular process element may be storedas metadata corresponding to the particular process element in theprocess element alignment map. The process element map may be stored inany desired or suitable data storage entity or plurality thereof that iscommunicatively connected a communication network of the process plant.A detailed description of said storage is provided in a later section.

The techniques, methods and systems described herein may be specificallyand particularly configured to automatically create a process elementalignment map or portion thereof. To illustrate, FIG. 3 illustrates anexample method 200 for automatically determining a process elementalignment map of a plurality of process elements used, in real-time orrun-time, to control at least a portion of a process in a process plant.In an embodiment, the method 200 is performed at least in part by anapparatus or system (e.g., one or more computing devices) particularlyconfigured with computer-executable instructions stored on one or moretangible, non-transitory computer-readable media that, when executed byone or more processors, cause the apparatus to perform one or moreportions of the method 200. It is noted that although the method 200creates a process element alignment map for a particular process flowwithin a process plant, the process plant may support multipleindependent flows, each of which may have a respective process elementalignment map. For example, a process plant may include a material feedflow, a hydrogen flow, and a steam flow. In some process plants,multiple independent flows may combine into an integral flow furtherdownstream.

In any event, the method 200 includes extracting, procuring, orotherwise obtaining a set of data from a plurality of data sourcesstoring and/or providing data descriptive of the plurality of theprocess elements (block 202) of the process plant. The plurality ofprocess elements are primary process elements of the process, andincludes a plurality of devices (e.g., wired or wireless field devices,controllers, input/output (I/O) devices, and other control devices), aplurality of process variables, a plurality of measurements or sensedconditions, and other process elements. Each of the plurality of primaryprocess elements is a primary source of process control within theprocess plant, e.g., is directly used, in real-time or during run-time,to control the process. Additionally, the obtained set of data includes,for each of the plurality of primary process elements, a respectiveidentification of the each process element and an indication of arespective physical location of the each process element in the processplant.

The plurality of data sources from which the data is obtained 202collectively stores and/or provides the respective identification of theeach process element and an indication of the respective physicallocation of the each process element in the process plant, but typicallya single data source included in this plurality of data sources is notsuitable as a lone or only source of data for defining the processelement alignment map. For example, some sources do not individuallyhave the complete or comprehensive set of data for all of the pluralityof primary process elements, or do not even indicate the entire set ofthe primary process elements. Some sources include other data that issuperfluous to primary process control, and thus, if used in a dataanalysis, would significantly increase the time and resources requiredto determine sources of variation of process elements.

In the method 200, the plurality of data sources from which the data isextracted, procured or obtained 202 includes multiple, different typesof data sources, such as one or more process flow diagrams (PFDs),Piping and Instrumentation Diagrams (P&IDs), loop diagrams, operatordisplay views, streamed data, and/or other data sources. Examples ofsome of these different types data sources is provided in each of FIGS.4A-4D. As will be discussed, as illustrated in these examples, it isevident how each different type of data source is not individuallysuitable as the only source for defining the process element alignmentmap.

Specifically, FIG. 4A is an illustration of a process flow diagram (PFD)210 of a portion of an example process in which a de-ethanizer column212 separates ethane from a gas. PFDs are generally utilized fordesigning a process and, as such, PFDs typically only indicate thegeneral piping or flows between a subset of the primary process elements(e.g., the major pieces of equipment in a plant) for expected, normaloperating conditions. PFDs, though, typically do not show alltransmitters and regulating valves that are required in implementationto control the process during different operating conditions. Forexample, the PFD 210 does not include any regulating process elementsthat monitor or detect whether or not one or more of the downstreamvalves 215 a, 215 b is sticking or is plugged, and that control thesplitter 218 or the input control valve 220 accordingly.

FIG. 4B is an illustration of a Piping and Instrumentation (P&ID)diagram 230 of a portion of an example process in which steam isprovided from a steam drum 232. P&IDs are generally created from PFDs,and thus include the primary piping and primary process elements, butalso include numerous other elements that are not primary elements, suchas local/field indicators, auxiliary valves and piping, and/or manuallyoperated block valves that are not connected or used in control of theprocess. For example, in the P&ID 230 of FIG. 4B, numerous primaryprocess elements are shown (e.g., process elements of the loop 301having tags LC301, LY301, LT301, LI301 to control steam that is outputfrom the drum 232). However, the P&ID 230 also includes other elementsthat are not primary process elements, such as the phosphate injectionmechanism 235 to mitigate scale adhesion and corrosion of the steam drum232, and the field instrumentation 238. If such non-processcontrol-affecting, extraneous elements were to be included in a dataanalysis used to determine sources of variation within the process,numerous inefficiencies in processing time and costs to perform theanalysis would be introduced. Further, in addition to includingextraneous elements which may add to analysis inefficiencies, in somesituations, a P&ID may not even include sufficient primary processelement information. In particular, while P&IDs may include indicationsof closed loop control (e.g., automatic regulation of the inputs to aprocess based on a measurement of an output of the process), details ofthe closed loop control implementation are typically not included inP&IDs.

FIG. 4C is an illustration of a loop diagram 245 of a portion of anexample process in which is used in a pulp mill. Loop diagrams generallyprovide detailed information that is needed to install or troubleshoot ameasurement and/or a valve or other device that performs some functionto control the process. The measurement and/or devices typically areassigned the same loop number (i.e., in FIG. 4C, the loop 18), and theloop diagram for the loop number includes indications of the devices,wiring, junction block termination, and other installation details.However, loop diagrams usually do not indicate how the measurementsand/or devices depicted therein relate or are connected to otherportions of the process, and in some cases, do not contain anydefinitions of how these measurements and devices are used to controlthe process. For example, the loop diagram 245 shows a flow measurementmade by measuring the differential pressure across an orifice plate 248(e.g., by using process elements having tags FE18, FT18 and FI18 of theloop 18), and the orifice plate 248 is sized to give a specific pressuredrop at a maximum flow rate of the process. However, the loop diagram245 does not indicate if and how the detected differential pressureaffects other portions of the pulp mill process. That is, the loopdiagram 245 does not indicate which other loops to which the loop 18connects to control the process, or how the pulp mill process iscontrolled based on the detected differential pressure FT18 either atthe maximum flow rate or at any other flow rate. Accordingly, a loopdiagram generally does not provide sufficient (if any) alignmentinformation between primary process elements across different loops toenable the determination of actual primary process element alignment.

Other possible data sources that may be used by the method 200 areoperator display views, such as the example operator display view 260depicted in FIG. 4D. As generally understood, an operator display viewtypically is configured to provide dynamic and/or static informationregarding the process and/or the process plant at a user interface. Forexample, the operator display view 260 is a display view that providesan operator the ability to monitor in real-time the performance of anaqueous phase preparation stage 261 in a batch process, and as such, thedisplay view 260 provides some information regarding indications of rawmaterial sources, tanks, valves, dryers, and the like along the primaryflow path of the process. The operator display view 260 also providesdynamic graphics that indicate, in real-time, changing values of thestage 261, e.g., how much material is in a particular tank 262 a,current tank temperature 262 b, flow rates 262 c, 262 d, etc. However,as seen in FIG. 4D, operator display views typically do not providesufficient (if any) alignment information between all primary processelements that are included in the area of the plant shown in the view,and operator display views typically do not provide sufficient (if any)alignment information across multiple views to enable the determinationof actual primary process element alignment. For example, the operatordisplay view 260 does not depict the process element that senses thetank temperature 262 c, and does not depict the control loop thatexecutes to maintain the tank temperature 262 c within a desired range.Further, although the operator display view 260 indicates that thebitumen preparation phase 265 is downstream of the aqueous preparationphase 261, the display view 260 does not indicate what process elementof the bitumen preparation phase 265 receives the output from the tank268 of the aqueous preparation phase, and how the flow rate of theoutput of the tank 268 is controlled.

It is noted that although only four types of data sources or diagramshave been discussed herein as being candidates for inclusion in theplurality of data sources from which the method 200 obtains primaryprocess element information (e.g., in FIGS. 4A-4D), one or more othertypes of data sources or diagrams may be additionally or alternativelyused by the method 200, such as plot plans, other to-scale diagrams,stored streamed data, logical diagrams or mappings, and/or other processdata sources. In some cases, a data source may be user input or amanually generated diagram.

Returning now to the method 200, as none of these different types ofdiagrams individually is able to provide and/or winnow to provide onlythe complete set of primary process elements of a process, the method200 extracts, procures, or otherwise obtains data 202 from a pluralityof different types of data sources. The obtained data includes at leastan indication of an identifier of each primary process element and anindication of a location (e.g., a physical, geo-spatial location) ofeach primary process element. In some cases, the indications of theidentifier and the location of a first primary process element may beobtained from a first P&ID diagram, while the indications of theidentifier and the physical location of a second primary process elementmay be obtained from a second P&ID diagram. For example, with referenceto FIG. 1, indications of identifiers and physical locations of primaryprocess elements associated with the continuous reactor 110 may beextracted from a P&ID for the continuous reactor 110, whereasindications of identifiers and physical locations of primary processelements associated with spray dryer tower 2 (reference 120 b) may beextracted from a different P&ID for the with spray dryer tower 2(reference 120 b). In an embodiment, the descriptive or indicativeinformation of two different primary process elements is distributivelystored across data sources in a mutually exclusive manner, e.g., theP&ID for the spray dryer tower 2 (reference 120 b) does not store anydescriptive or indicative information of the process elements FT301,FC301 and FV301 controlling the input flow 128 into the continuousreactor 110, and the P&ID for the continuous reactor 110 does not storeany descriptive information for the process elements TT323, TC323, TV323controlling the temperature inside the tower 2 (reference 120 b).

In some cases, an indication of an identifier of a particular primaryprocess element is obtained from a first data source, while anindication of the location of the particular primary element may beobtained from a second data source. For example, the tag of a particularprimary process element may be extracted from a PFD, whereas anindication of the geo-spatial physical location may be extracted from anoperator display view that presents the geo-spatial layout of equipmentin the process plant.

Indeed, the method 200 may extract or obtain 202 at least some of theprimary process element data from two or more of any number of differenttypes of data sources, e.g., from two or more of a PFD, a P&ID, a loopdiagram, stored streamed data, manually generated data, or an operatoror display view, each of which corresponds to a respective at least aportion of the process or of the process plant. The method 200 mayadditionally or alternatively extract or obtain 202 at least some of theprimary process element data from two or more instances of a same typeof data source. For example, the method 200 may extract or obtain 202the primary process element data from two or more operator display viewsor from two or more loop diagrams, each of which is associated with aspecific portion or view of the process or of the process plant.

To illustrate example concepts and techniques associated with theprocurement of primary process element information 202, FIG. 5 includesan example flow 270 of data to generate a process element alignment map.In FIG. 5, data descriptive or indicative of a plurality of primaryprocess elements of a process plant is stored across or provided bymultiple diagrams or data sources 272, 275, 276, 277, 278. For example,some of the example primary process element data is stored in one ormore engineering drawings 272, such as one or more P&IDs 272 a, one ormore process flow diagrams 272 b, and/or one or more loop diagrams 272c. Additionally, some of the example primary process element data isstored in one or more control system configurations 275, such as one ormore control module configurations 275 a, one or more operator views ordisplays 275 b, and/or one or more device configurations 275 c (e.g.,configurations of controllers, I/O devices, field devices, etc.).Further, streamed data 276 from one or more other process elements ordevices may provide data descriptive or indicative of the plurality ofprocess elements. The streamed data 276 may be stored streamed data(e.g., obtained streamed data that has been stored in a centralized bigdata appliance and/or in an embedded big data appliance), or thestreamed data 276 may be a real-time stream. Still further, manuallygenerated inputs 277 such as manually generated charts, diagrams, ormappings and/or user input may provide data descriptive or indicative ofthe plurality of process elements. At least some of the primary processelement data may be stored in or provided by one or more other datasources 278, such as plot plans or other diagrams.

The primary process plant data stored in and/or provided by thesemultiple data sources 272, 275-278 is obtained or extracted 202, forexample, by one or more applications 280 executing on one or morecomputing devices associated with the process plant. For example, theapplications 280 may extract data from a stored data source, or theapplications 280 may extract, filter, or otherwise obtain data providedby a real-time streaming data source. As illustrated in FIG. 5, theobtained data 282 includes indications of identifications of the variousprimary process elements, such as tags and names. The obtained data 282also includes indications of physical locations of the various primaryprocess elements, such as area sheet and loop numbers, or GPScoordinates. The one or more applications 280 are configured to sort,order, or organize the obtained data 282 based on physical location todetermine a relative ordering 285 of the primary process elements withinthe flow of the process, e.g., the process element alignment map. InFIG. 5, the relative ordering 285 is displayed in an ordered listing,however, this embodiment is only one of many embodiments in which therelative ordering is reflected. For example, the relative ordering ofeach particular primary process element may be reflected by a code, tag,or other indicator.

Returning to FIG. 3, in some embodiments, the method 200 may includedetermining one or more members of the set of primary process elements(e.g., as previously discussed with respect to FIG. 1). For example, fora first particular data source, first a determination of the subset ofprimary process elements whose descriptive data is at least partiallystored in the first particular data source may be made (e.g., by type ofprocess element and/or by type of data), and then only the descriptiveor indicative data for that subset of primary process elements isextracted or obtained 202. In another example, a general extraction ofdata from a second particular data source may be performed 202, and thenthe extracted data 202 may be whittled to a subset based on thedetermination or identification of the primary process elementsrepresented therein.

At block 205, the method 200 includes causing the extracted or obtaineddata to be stored. For example, the method 200 may cause at least someof the extracted or obtained data to be stored temporarily, e.g., in acache or in re-writable storage, and/or the method 200 may cause atleast some of the extracted or obtained data to be stored on a longerterm basis, such as at a local or remote big data appliance storage areaor at a perpetual data storage area.

With further respect to blocks 202 and 205 and as discussed above, insome cases, for at least some of the primary process elements the method200 also includes extracting or obtaining 202 other descriptive orindicative information corresponding to the primary process elements,such as GPS coordinates, an image of the primary process element or itsimmediate environs, a model/make/serial number, and the like. Such otherdescriptive or indicative information may be stored 205 as metadata, ifdesired, e.g., as metadata associated with the indications of theidentifier and the physical location of the primary process element.

At block 290, the method 200 includes determining the process elementalignment map for the process based on the obtained set of data. Asdiscussed above, the process element alignment map indicates primaryprocess elements for the process, and include or indicates a respectiveidentification of each primary process element as well as a respectiveorder of each primary process element with respect to one or more otherprimary process elements. For example, for each primary process element,the process element map includes a respective indication of a respectiveorder of an occurrence of an event at the each primary process elementto control the process relative to an occurrence of another event atleast one other primary process element to control the process. As such,to determine the process element alignment map 290, the method 200determines the relative connection ordering or alignment of some or allof the primary process elements within the flow of the process.

In some scenarios, the determined process element alignment map may bestored (block 292). For example, the method 200 may cause at least someof the determined process element alignment map to be stored 292temporarily, e.g., in a cache or in re-writable storage, and/or themethod 200 may cause at least some of the determined process elementalignment map to be stored 292 on a longer term basis, such as at alocal and/or remote big data appliance storage area or at a perpetualdata storage area.

In some scenarios, the method 200 includes causing at least a portion ofthe determined process element alignment map to be delivered to anotherapplication and/or to another computing device 295. In some scenarios,the method 200 additionally or alternatively includes causing at least aportion of the determined process element alignment map to be presentedat a user interface 295, such as displayed on a local or a remotedisplay, on one or more operator views, or printed on a report. The atleast the portion of the process element alignment map may be presentedin any desirable format, such as in a table format, a graph format, orsome other format, such as previously discussed.

In an example scenario, the at least a portion of the determined processelement alignment map is presented 295 at a user interface, e.g., as adraft process element alignment map. User input including user-indicatedchanges to the draft process element alignment map is received, and thedraft process element alignment map is modified based on the receiveduser input. For example, a user may indicate that a new primary processelement that has not yet been configured for the process control systemis to be added to the map, the user may add additional descriptive datato the map (e.g., attaching a newly captured image as metadata for aparticular element), or the user may indicate that a particular group ofprimary process elements are to be removed from the map as they aretemporarily out-of-service. The modified process element alignment mapmay be stored 292, presented at a user interface, and/or caused to bedelivered to another application and/or computing device 295.

Referring again to the example process 102 shown in FIG. 1, it isapparent that for a given process variable, certain process variablesand/or measurements have a stronger alignments or associations with thegiven process variable (e.g., have a greater impact or effect on thebehavior of the given process variable), while other process variableshave a weaker alignments or associations (e.g., have a lesser impact oreffect of the behavior of the given process variable), if any at all.For example, referring to the control strategy executed by thecontroller LC307, the measurement of the current amount of reactants asdetermined by the sensor LT307 is extremely aligned or associated withthe controller LC307, as the measurement LT307 is a direct input intothe control algorithm executed by the controller LC307. The amount ofleftover solids from the reactor 110 as detected by the sensor AT303also has an alignment or association with the with the controller LC307,albeit a less strong one than that of the measurement performed bysensor LT307, as AT303 first directly affects the amount of sodiumbicarbonate released from the stock 108 which then is provided into thereactor 110 to react to form the contained reactants therein, thereforeaffecting the level of the contained reactants LT307 which is providedto the controller LC307. On the other hand, measurements or processvariables that are sufficiently downstream in the process elementalignment map from the controller LC307 typically have no or negligibleassociation or alignment with process variables that are controlled bythe controller LC307. For example, a detected pressure PT311 of a spraydryer tower 120 a has negligible, if any, effect or impact on thecontroller LC307, and a weight of the end-product measured by WT315 alsohas negligible, if any, effect on the controller LC307.

In known process control plants and systems, when evidence of a fault oran abnormal condition or variation occurs, typically an operator,instrument technician or process engineer uses his or her knowledge ofthe process being controlled by the system in combination with ananalytics tool to determine upstream measurements and process variablesthat may have contributed to the production of the evidence of the faultor the abnormal condition/variation, e.g., that are sources of the faultor the abnormal condition/variation. To illustrate, referring to FIG. 1in an example scenario, the controller LC307 detects that the level ofreactants contained within the reactor 110 has been above a desiredthreshold for a pre-determined amount of time, and the controller LC307causes an alert or alarm to be generated. Upon being alerted to thealert or alarm, an operator attempts to determine the cause of thiscondition by providing, to an analytics tool, indications ofmeasurements and/or process variables that he or she feels may becontributing to this condition. Based on the input data, the analyticstool performs an analysis such as Principal Component Analysis (PCA) todetermine the relative effects of the input measurements and/or processvariables on the abnormal behavior detected by LC307, and the analysistool provides this information to the operator so that he or she canfurther investigate the condition.

Having an operator use an analytics tool to determine the source of thevariation, though, suffers from incompleteness, as well as beingcumbersome and requiring a relatively large amount of time to execute.For example, having an operator use an analytics tool is dependent onthe extent of the operator's knowledge of the process, which may not becomplete, thorough, or accurate. The operator typically consultsdiagrams of the process plant to augment the operator's knowledge (e.g.,PI&Ds, Process Flow Diagrams (PFDs), loop diagrams, etc.), however, saiddiagrams each contain a different perspective of the process plant andthe process control system provided in the plant, and are notsufficiently detailed for the operator to accurately determine anelemental process element alignment map of the process.

However, using the techniques, systems and methods described herein, oneor more sources of the fault or the abnormal variation/behavior areeasily and accurately determined, and can be consistently determinedirrespective of particular operators' knowledge and practice. Forexample, FIG. 6 illustrates an example method 310 of automaticallydetermining one or more sources of a variation of a behavior of a targetprocess element used in a process plant to control a process inreal-time or during run-time. In some situations, the method 310operates in conjunction with the method 200, for example, one or moreportions of the method 310 may be pre-pended, appended, and/orinterleaved with elements of the method 200. The method 310 may beexecuted or performed by the same set of computing devices that performsthe method 200, or the method 310 may be executed by a different ofcomputing devices from those performing the method 200.

In an embodiment, the method 310 is performed at least in part by anapparatus or system (e.g., one or more computing devices) particularlyconfigured with computer-executable instructions stored on one or moretangible, non-transitory computer-readable media that, when executed byone or more processors, cause the apparatus to perform one or moreportions of the method 310. For example, at least a portion of themethod 310 may be performed by one or more applications hosted by one ormore computing devices.

The method 310 includes receiving 312 an indication of a particular ortarget process element of the process control system or process plant.The target process element is one of a plurality of process elementsused to control a process during run-time or real-time, and may be afield device, a measurement, a process variable, or other processelement. Typically, but not necessarily, the target process element is aprimary process element at which faults, abnormal operations, and/orvariations in behavior have been detected or observed. Additionally, anindication of the target process element may be of any suitable format,for example, a tag, an identification number or code, a reference, anaddress or location, a flag, a value, or any other data that isindicative of the target process element.

In some cases, the indication of the target process element is received312 from an application, e.g., from a user-interface application oranother application executing on the same or different set of computingdevices on which the method 310 is executed. For example, auser-interface application receives an indication of the target processelement from a user (e.g., via a user interface when an operatoridentifies a fault or a variation in the behavior of the target processelement, and desires to obtain information about possible sources orcauses of the fault or variation), and the user-interface applicationforwards said indication so that it is received 312. In another example,the indication of the target process element is received from anotherapplication, such as by a learning, discovery, training, or otheranalytics application.

In some scenarios, the learning, discovery, training, or analyticsapplication is an unsupervised application, that is, the applicationinitiates and executes without and/or independent of any user input. Theunsupervised application may be a machine learning or predictiveanalysis application executing on the same or different set of computingdevices on which the method 310 is being executed. Additionally oralternatively, the unsupervised application may be a data mining or datadiscovery application executing on the same or different set ofcomputing devices on which the method 310 is being executed. In somecases, the unsupervised application operates on locally stored,distributed big data of a process control system (e.g., at a distributedbig data device of the process control system), or operates on centrallystored big data of the process control system (e.g., at a centralizedbig data appliance of the process control system). Some examples ofunsupervised learning, discovery, training, or analytics applicationsthat are used on centrally stored big data in process plants and processcontrol systems and that may operate in conjunction with any or all ofthe methods, techniques and systems described herein are found inaforementioned U.S. patent application Ser. No. 13/784,041 entitled “BIGDATA IN PROCESS CONTROL SYSTEMS.” Some examples of unsupervisedlearning, discovery, training, or analytics applications that are usedon locally and distributively stored big data in process plants andprocess control systems and that may operate in conjunction with any orall of the methods, techniques and systems described herein are providedin aforementioned U.S. patent application Ser. No. ______, filedconcurrently herewith and entitled “Distributed Big Data in a ProcessControl System” (Attorney Docket No. 06005/592952). Further, in someembodiments, at least a portion of method 310 and/or of the othermethods described herein is performed by one or more centralizedlearning, discovery, training, or analytics applications (which may ormay not be unsupervised) at a centralized process control system bigdata appliance. Additionally or alternatively, at least a portion ofmethod 310 and/or of the other methods described herein is performed byone or more distributed learning, discovery, training, or analyticsapplications (which typically, but not necessarily, is unsupervised) ata distributed or embedded big data appliance that is included in aprocess control device of the process control plant. In an example, aprimary process element includes an embedded big data appliance.Distributed and centralized big data in process control plants andsystems is discussed in more detail in a later section.

The method 310 includes determining 315, using at least a portion of aprocess element alignment map of the process, a set of primary processelements that are upstream of the target process element, e.g.,“upstream process elements.” Typically, but not necessarily, the set ofprimary process elements that are upstream of the target process elementis a subset of the entire, comprehensive, or complete set of primaryprocess elements that are used to control the process, and each of theset of upstream process elements is ordered in the process elementalignment map ahead of (and in some cases, adjacent to) the targetprocess element.

The process element alignment map used in the determination 315 of theupstream process elements may have been generated by the method 200, ormay have been generated by another suitable method. In some scenarios,at least part of the process element map may have been generatedmanually. As previously discussed, the process element alignment mapincludes indications of primary process elements and their relativelocations or order within the process flow. Thus, using the targetprocess element that was received 312 as a reference or entry point ofthe process element alignment map, the process elements that are orderedahead of the target process element are determined 315 to be the set ofupstream elements corresponding to the target process element. It isnoted that entire process element alignment map is not required to besearched or used to determine the upstream elements. For example, areasor other portions of the process or process plant that are identified asbeing significantly downstream of the process plant need not be searchedor utilized.

In some embodiments, such as when the method 310 is performed by anunsupervised application, the method 310 includes determining at least apart of the process element alignment map based on the received targetprocess element (reference 312), for example, by using at least aportion of the method 200 in conjunction with the method 300. Asdiscussed above, the method 200 for automatically determining a processelement alignment map of a process includes extracting or obtaining data202 from multiple data sources which, in some embodiments, may includeextracting or obtaining the data 202 for all primary process elements ofthe process. However, when the method 200 is used in conjunction withthe method 300 and an indication of a target process element is received312, the data that is extracted or obtained 202 from the multiple datasources may be only a subset of all of the data for all primary processelements. For example, only data corresponding to an area in which thetarget process element is included or corresponding to an upstream areaof the area in which the target process element is included may beextracted or obtained 202.

In an illustrative but non-limiting example, referring to FIG. 1,suppose an amplitude range or a frequency of variation of a measurementtaken by sensor TT305 is determined to be abnormal. As seen in FIG. 1,and as indicated in a process element alignment map of the process 102of FIG. 1, sensor TT305 is located between the area of the continuousreactor 110 and the area of the surge tank 118. Thus, when obtainingdata 202 to determine the process element alignment map as part of themethod 310, only data corresponding to the area of the process plant ofthe continuous reactor 110 and the area of the surge tank 118 isobtained, while data corresponding to the areas of the spray dryertowers120 a, 120 b need not be obtained. Consequently, only the portionof the process element alignment map that corresponds to the TT305(e.g., the target process element) is determined (block 290 of FIG. 3),and only this determined portion of the map is used to determine (block315 of FIG. 6) the upstream process elements of TT305. In such a manner,time, processing, and other resources are optimized as compared withdetermining and using the complete, comprehensive process elementalignment map for the entire process.

Returning to FIG. 6 and the method 310, one or more data analyses areperformed on stored data corresponding to the upstream process elementsand the target process element (reference 318). Generally, stored datathat has been collected over time for each upstream process element andfor the target process element (e.g., historical real-time data that hasbeen collected for each upstream process element and for the targetprocess element as a result of the elements operating to control theprocess) is provided as inputs to the data analysis. At least somestored data is stored big data, for example. In some cases, the method310 prevents data corresponding to any process element that is notupstream with respect to the target process element from being used asan input to the data analysis so that the data analysis is performedonly on the data of the upstream process elements. In such cases, bylimiting the input data set, the time, processing, and other resourcesrequired to determine the sources of variation of the target processelement may be decreased or minimized.

In some embodiments, the data analysis operates on its inputs todetermine time offsets of the upstream process elements with respect tothe target process element. A “time offset,” as generally used herein,refers to an offset from a time at which a particular event occurs orbehavior is observed (e.g., change in measurement) at an upstreamprocess element to a time at which the effect of the particular upstreamevent or observed behavior is reflected at a downstream element, such asby an occurrence of a related particular event or a particular change inbehavior of the downstream element. In these cases, the data analysisoperates on the stored historical data to determine the time offsetscorresponding to the occurrences of various events or behaviors at oneor more upstream process elements with respect to the occurrences oftheir respective effects or behaviors at the target process element. Thedata analysis to determine time offsets of upstream process elements mayutilize cross correlation, principal component analysis (PCA), partialleast squares regression analysis (PLS), and/or other suitable analyticstechnique.

In some embodiments, a data analysis (e.g. the data analysis todetermine the time offsets described above, or another data analysis)operates on its inputs to determine respective strengths of impact ofthe upstream process elements on the behavior (or variation thereof) ofthe target process element. For example, given the set of upstreamprocess elements, the data analysis may operate on historical dataobtained from each of the upstream process elements over some given orpredetermined period of time to estimate the degree of association ofeach upstream element and the target process element, e.g., the strengthof impact of each upstream element on the behavior of the target processelement. To illustrate using just one of many examples, based on thehistorical data, the data analysis estimates a degree of percentvariation of the behavior of each upstream element associated with thedegree of percent variation of the behavior of the target processelement. Using the estimations, the upstream elements causing thegreater percent variations in the behavior of the target process elementare determined to have higher degrees of association or strengths ofimpact on the behavior of the target process element, and these upstreamelements causing the greater percent variations are included in a modelgenerated by the data analysis. Upstream elements having a degree ofassociation or strength of impact that is significantly less than athreshold (e.g., a percentage of total variation of the target processelement behavior) may be excluded from the model. In some instances, thethreshold is determined based on heuristic data and/or desired or actualmodel quality.

In some embodiments, a data analysis (e.g., one of the data analysesdescribed above, or another data analysis) operates to determine theprocess impact delays of upstream process element with respect to thetarget process element. As used interchangeably herein, the “processimpact delay” or “impact delay” of a particular upstream process elementwith respect to the target process element generally refers to an amountof delay, during operation and control of the process, between acontribution or involvement of the particular upstream process elementand a resulting contribution or involvement of the target processelement. For example, the data analysis to determine the process impactdelays determines the degree of percent variation of the behavior of theparticular upstream element with respect to the degree of percentvariation of the behavior of the target process element for the varioustime offsets corresponding to the particular upstream process element.The time offsets corresponding to the particular upstream processelement may have been determined by the techniques described above, orby other suitable techniques. The particular time offset causing thegreatest percent variation in the behavior of the target process elementis determined to be the process impact delay of the particular upstreamelement with respect to the target process element. The data analysis todetermine impact delays of upstream process elements may utilize anysuitable analytics technique, such as cross correlation, principalcomponent analysis (PCA), partial least squares regression analysis(PLS), and/or other technique.

The method 310 includes determining, identifying, or selecting 320 oneor more upstream process elements as a source of the variation of thebehavior of the target process element, e.g., based on the time offsets,the strengths of impacts, and/or the impact delays determined by thedata analysis or analyses. For example, upstream process elements thathave non-negligible impact delays are eligible candidates of sources ofvariation, and the determination or selection of the one or more sourcesfrom the pool of eligible candidates may be winnowed or finalized basedon a threshold. In an example, only the upstream process elements havinga respective impact delay equal to or surpassing a threshold (e.g., acontribution greater than X %, the top Y contribution percentages, onlyprocess elements added to the process plant only after a certain date,etc.) are identified or indicated as a source of variation. Thethreshold may be predefined, and/or the threshold may be configurable.

The method 310 includes causing the one or more sources of the variationof the behavior of the target process element to be indicated,identified, or provided (block 322). In some cases, the indications orthe identifications of the one or more sources are provided 322 to auser interface. Additionally or alternatively, the indications or theidentifications of the one or more sources of behavior variation in thetarget process element are provided 322 to an application, e.g., to anapplication that is executing on the same set of computing devices onwhich the method 310 is operating, or to an application that isexecuting on another set of computing devices. For example, theidentifications or indications of the one or more sources of variationsof the target process element may be provided 322 to a requestingapplication, e.g., an application from which the indication of thetarget process element was received 312. In some scenarios, therequesting application is an unsupervised learning, discovery, training,or analytics application operating on big data within the process plant.

In some situations, each of the one or more sources of variation of theparticular or target process element is indicated, provided, oridentified to the recipient application in conjunction with otherdescriptive or indicative information of the source, such as itsabsolute geo-spatial location within the process plant,make/model/serial number, an image, etc. In some situations, anindication of the relative contribution of the each source of variationto the changes in behavior of the target process element is indicated orprovided. FIG. 7 illustrates one example display 330 that provides therelative contributions of upstream process elements (e.g., the sourcesof variations) to a target process element using bar graphs on a screendisplay. In FIG. 7, the example display 330 includes unexplained andexplained process variations of primary process elements that impact ameasure of process quality 332, which, in this example, is the targetprocess element (e.g., as indicated at the block 312 of the method 310)at which variations have been observed. In particular, the examplecontribution graph 335 includes a bar chart 338 that displays thecontributions of explained variations (e.g., solid black bars or barportions) and unexplained variations (e.g., striped bars or barportions) of each upstream primary process element 340 a-340 h to theprocess quality variation 332. Additionally, the primary processelements 340 a-340 h are organized on the chart 338 by displaying theelement having the greatest contribution to the overall processvariation 332 at the top of the chart 338 (e.g., Media Flow 340 a). Inother examples, the upstream primary process elements 340 a-340 h may beorganized by preferences of a process control operator, or in anydesired manner.

Each of the upstream primary process elements 340 a-340 h within the barchart 338 includes a numerical value associated with a variation of theprocess element. For example, the −2.33 value associated with the MediaFlow process element 340 a may indicate that a fluid is flowing 2.33gal/sec slower than a mean value or a threshold value. Alternatively,the −2.33 value may indicate a statistical contribution amount that theMedia Flow process element 340 a contributes to the variation of theprocess quality 332.

In FIG. 7, the explained and unexplained variations for each primaryprocess element 340 a-340 h are superimposed or shown together toprovide an easy graphical display for a process control operator. Theexplained variations of each process element 340 a-340 h may becalculated, for example, by utilizing a model variation T2 statistic,and the unexplained variation of each primary process element may becalculated, for example, by utilizing a Q statistical test. In otherexamples, the bar chart 338 may display other values of the primaryprocess elements 340 a-340 h, e.g., the mean values of the elements,standard deviations for each of the elements, and/or other outputs thathave been generated based on performed data analyses.

The example contribution graph 335 includes a process informationfaceplate 342 that displays process information, process time of thedisplayed variations, and numerical values of explained and unexplainedvariations for a selected process element 340 a-340 h. For example, inFIG. 7, the process information faceplate 342 shows that at 12:38:26P.M., the Media Flow element 340 a has a 0.26 explained contribution anda 2.89 unexplained contribution to the overall process quality variation332. Additionally, the contribution graph 335 includes an advised actionfaceplate 345. The advised action faceplate 345 may display processcorrection recommendations to remediate a detected process fault. Forexample, in FIG. 7, the advised action faceplate 345 recommendsimproving the variation in the process quality 332 by increasing theMedia Flow by 1.85 gal/s in the field device FIC3, which may correspond,for example, to a valve or a pump within the process control system 106that is capable of modifying the Media Flow rate. Additionally, theadvised action faceplate 345 includes a recommendation to inspect a FlowMeter FIT3. By inspecting the Flow Meter FIT3, an operator may determineif the Flow Meter is outputting an accurate Media Flow value. Theadvised actions displayed in the faceplate 345 may have been determinedby one or more data analyses, for example.

Of course, while FIG. 7 illustrates one example of a screen 330displaying sources of variation of a target process element, otherrepresentations of indications or identifications of sources ofvariation of the target process element may be provided, such as a trenddisplay view, an alphanumeric report, directed graph, other types ofgraphics, any type of data file, and/or any other desired format. A usermay be able to manipulate aspects of the display, if desired. Forexample, a user may be able to sort, filter, select, or otherwiseorganize the display and/or the information corresponding to the one ormore sources of variation.

In some scenarios, the indication of the target process element, itscorresponding portion of the process element alignment map, thedetermined one or more sources, and/or their relative contributions arestored (not shown), for example, in a database, locally at a computingdevice on which the method 310 is operating, in a local embedded bigdata appliance, or at another remote data storage device such as acentralized big data appliance or other centralized system data storagearea. An operator or user may provide an identifier or name for any orall of said stored data. For example, referring to FIG. 7, the operatoror user may provide a name or identifier for the fault or abnormalbehavior detected at AT360 (e.g., “AT360-F05”), and optionally mayprovide a description (e.g., “greater than 5% swing over 24 hoursdetected on Apr. 28, 2013, 8:04 a.m.”). The operator may also cause theidentifications and relative contributions of FT302, TT345 and TT355 bestored in conjunction with the stored fault AT360-F05.

As mentioned above, in some situations, at least some portions of themethod 200 of FIG. 3 and the method 310 of FIG. 6 are combined into anintegral method for determining one or more sources of a variation inbehavior of a target process element that is operating to control aprocess in a process plant. To illustrate, FIG. 8 includes a blockdiagram of an example method 350 that combines portions of the methods200 and 310. It is noted that although the method 350 includes selectedportions of the methods 200 and 310, in some embodiments, other portionsof the methods 200 and 310 are additionally or alternatively combined.Additionally or alternatively, in some embodiments, at least someportions of the method 350 illustrated in FIG. 8 are omitted. In thediscussion below, portions of the method 350 are discussed using theirreference numbers originally presented in the description of FIGS. 2 and6.

In FIG. 8, the method 350 includes receiving an indication of the targetprocess element 312, and defining at least a portion of a processelement alignment map 290 of a process that is controlled in part by thetarget process element. For example, the at least the portion of theprocess element is determined based on the target process element. Themethod 350 includes obtaining or extracting data 202 for primary processelements included in the defined at least a portion of the processelement alignment map. For example, data corresponding to primaryprocess elements in areas of the process plant that are upstream of andthat include the target process element is obtained or extracted 202.

Based on the determined at least the portion of the process element mapand the obtained data, process elements that are upstream of the targetprocess element are identified 315. Additionally, the respective timeoffsets, strengths of impact, impact delays and/or relativecontributions of the upstream elements on the target process element aredetermined by using one or more data analyses 318 on data that has beenstored over time and that was generated as a result of controlling theprocess in real-time over time (e.g., historical data). For example, thedata analysis 318 may be performed on historical big data collected forthe upstream process elements and for the target process element. One ormore particular upstream process elements are determined or identified320, using the results or outputs of the data analysis or analyses, tobe the sources of variation in the behavior of the target processelement, for example, based on their respective impact delays, athreshold, and/or other criteria. The determined one or more sources ofthe variation in the behavior of the target process element are causedto be indicated, provided, or identified 322 as sources of variation inthe behavior of the target process element.

Any or all of the systems, methods, and techniques disclosed herein maybe utilized in any process plant that is configured to control a processin real-time. The process plant may include, for example, one or morewired communication networks and/one or more wireless communicationnetworks. Similarly, the process plant may include therein one or morewired process elements and/or one or more wireless process elements. Theprocess plant may include centralized databases, such as continuous,batch and other types of historian databases.

Some of the process plants in which at least portions of the systems,methods, and techniques disclosed herein are utilized include a processcontrol big data network and process control big data network devices.For example, at least some of the systems, methods, and techniquesdisclosed herein are implemented in a process plant that supportscentralized big data, such as described in aforementioned U.S.application Ser. No. 13/784,041. Such a process plant includes acentralized big data appliance, and at least some of the techniquesdisclosed herein may be implemented by the centralized big dataappliance. For instance, at least portions of the method 200, the method310, and/or of the method 350 may be executed by a centralized big dataappliance.

Additionally or alternatively, at least portions of the systems,methods, and techniques disclosed herein are utilized in a process plantthat supports distributed or embedded big data, such as described inaforementioned co-pending U.S. patent application Ser. No. ______, filedconcurrently herewith and entitled “DISTRIBUTED BIG DATA IN A PROCESSCONTROL SYSTEM.” As such, a process plant may include one or moredistributed big data devices, each of which includes a respectivedistributed or embedded big data appliance. An embedded big dataappliance may implement at least some of the techniques disclosedherein, for example, an embedded big data appliance may implement atleast a portion of the method 200, at least a portion of the method 310,and/or at least a portion of the method 350. In some cases, one or moreof the distributed big data devices are communicatively connected by aprocess control big data network.

To illustrate, FIG. 9 is a block diagram including various exampleaspects of an example process plant or process control system 40 inwhich the techniques, methods, systems and apparatuses disclosed hereinmay be implemented and included. In FIG. 9, distributed big data devicesare indicated by a “DBD” reference that signifies the inclusion of arespective embedded big data appliance 530 therein to locally manage bigdata, and centralized big data devices are indicated by a “CBD”reference, signifying that at least some of the big data collected bythe CBD device is transmitted for storage at a centralized big dataappliance 408. In some process plants, the centralized big dataappliance 408 is the process control big data appliance described inaforementioned U.S. patent application Ser. No. 13/784,041, althoughother process control big data appliances may be additionally oralternatively utilized. Generally, the process control big dataapparatus or appliance 408 is centralized within the system 40, and isconfigured to receive and store data (e.g., via streaming and/or via oneor more other protocols) from the centralized big data nodes (CBD).Optionally, the process control centralized big data apparatus orappliance 408 also receives and stores data from one or more distributedbig data nodes (DBD).

In FIG. 9, the process control big data devices CBD and DBD and theprocess control centralized big data appliance 408 are nodes of aprocess control big data network 400, and are communicatively connectedvia a process control system big data network backbone 405. The backbone405 includes a plurality of networked computing devices or switches thatare configured to route packets to/from various process control big datadevices CBD and DBD and to/from the process control big data appliance408. The plurality of networked computing devices of the backbone 405may be interconnected by any number of wireless and/or wired links, andthe big data network backbone 405 may support one or more suitablerouting protocols, e.g., protocols included in the Internet Protocol(IP) suite (e.g., UPD (User Datagram Protocol), TCP (TransmissionControl Protocol), Ethernet, etc.), or other suitable routing protocols,which may be public or proprietary. In an embodiment, at least some ofthe nodes connected to the backbone 405 utilize a streaming protocolsuch as the Stream Control Transmission Protocol (SCTP).

With particular regard to the process control centralized big dataappliance 408, and referring to FIG. 10A, an example process controlcentralized big data apparatus or appliance 408 includes a data storagearea 502 for historizing or storing the data that is received from theprocess control centralized big data devices CBD (and optionally some ofthe process control distributed big data devices DBD), a plurality ofcentralized appliance data receivers 505, and a plurality of centralizedappliance request servicers 508. The process control system big datastorage area 502 may comprise multiple physical data drives or storageentities, such as RAID (Redundant Array of Independent Disks) storage,cloud storage, or any other suitable data storage technology that issuitable for data bank or data center storage. However, to the big datanodes CBD and DBD of the network 400, the data storage area 502 has theappearance of a single or unitary logical data storage area or entityand is addressable as such. Consequently, the data storage 502 is viewedas a centralized big data storage area 502. The structure of thecentralized big data storage area 502 supports the storage of any typeof process control system related data, including historical data. In anexample, each entry, data point, or observation of the data storageentity includes an indication of the identity of the data (e.g., source,device, tag, location, etc.), a content of the data (e.g., measurement,value, etc.), and a time stamp indicating a time at which the data wascollected, generated, received or observed. As such, these entries, datapoints, or observations are referred to herein as “time-series data.”The data may be stored in the data storage area 502 using a commonformat including a schema that supports scalable storage, streamed data,and low-latency queries, for example.

In addition to the centralized big data storage 502, the process controlsystem centralized big data appliance 408 includes one or morecentralized appliance data receivers 505, each of which is configured toreceive data packets from the network backbone 405, process the datapackets to retrieve the substantive data and timestamp carried therein,and store the substantive data and timestamp in the data storage area502. The centralized appliance data receivers 505 may reside on aplurality of computing devices or switches, for example.

The process control system big data appliance 408 also includes one ormore centralized appliance request servicers 508, each of which isconfigured to access time-series data and/or metadata stored in theprocess control system centralized big data appliance storage 502, e.g.,per the request of a requesting entity or application. The centralizedappliance request servicers 508 may reside on a plurality of computingdevices or switches, for example. In an embodiment, at least some of thecentralized appliance request servicers 508 and the centralizedappliance data receivers 505 reside on the same computing device ordevices (e.g., on an integral device), or are included in an integralapplication. Referring to some of the techniques disclosed herein asillustrative examples, one or more of the centralized appliance requestservicers 508 may retrieve some of the stored data per the request of adata analysis application to determine at least a portion of a processelement alignment map (block 290 of FIG. 3). A centralized appliancerequest servicer 508 may retrieve some of the stored data per therequest of a data analysis application to determine upstream processelements of a target process element (block 315) of FIG. 6. Acentralized appliance request servicer 508 may retrieve some of thestored data per the request of a data analysis application to determineone or more source(s) of behavior variation of a target process element(blocks 318, 320 of FIG. 6), or per request of other data analysesperformed by the methods, systems, apparatuses and techniques describedherein. In some cases, a data analysis performed by the methods,systems, apparatuses and techniques described herein is performed by oneor more of the centralized appliance request servicers 508.

Turning now to the centralized big data devices CBD of FIG. 9,generally, each centralized big data device CBD is configured tocollect, store and transmit, to the centralized big data appliance 408,data (e.g., big data) corresponding to a process plant and/or to aprocess controlled in the process plant for historization or long-termstorage. The centralized process control big data device CBD may be adevice as described in aforementioned U.S. patent application Ser. No.13/784,041, although other centralized big data devices may beadditionally or alternatively utilized in conjunction with thetechniques described herein. The CBD may be a process controller, afield device, an I/O device, a user interface device, a networking ornetwork management device, or a historian device whose primary functionis to temporarily store data that is accumulated throughout the processcontrol system 400, and to cause the delivered data to be historized atthe centralized big data appliance 408.

As shown in the block diagram of FIG. 10B, an example centralized bigdata device CBD is configured to collect real-time data that is directlytransmitted by the device CBD and/or directly received at the deviceCBD, and to transmit the collected data for historization at thecentralized big data storage appliance 408, e.g., via an interface 510to the process control system big data network 405. Some centralized bigdata devices CBD include another interface 512 via which process controldata is transmitted to and received from a process control communicationnetwork 515 (e.g., by using a process control protocols such asFOUNDATION® Fieldbus, HART®, the WirelessHART®, PROFIBUS, DeviceNet,etc.) to control a process executed by the process plant or system 400,and at least some of the data sent and received over the process controlinterface 512 is collected and temporarily stored or cached at thecentralized big data device CBD.

In addition to the interfaces 510, 512, the centralized big data deviceCDB includes a multi-processing element processor 518 configured toexecute computer-readable instructions, a memory 520, and a cache and/orflash memory 522. The multi-processing element processor 518 is acomputing component (e.g., an integral computing component) having twoor more independent central processing units (CPU) or processingelements 518 a-518 n, so that the multi-processing element processor 518is able to perform multiple tasks or functions concurrently or inparallel by allocating multiple calculations across the multipleprocessing elements. The memory 520 of the device CDB includes one ormore tangible, non-transitory computer-readable storage media, and maybe implemented as one or more semiconductor memories, magneticallyreadable memories, optically readable memories, molecular memories,cellular memories, and/or any other suitable tangible, non-transitorycomputer-readable storage media or memory storage technology. The memory520 uses mass or high density data storage technology, in an example.The memory 520 stores one or more sets of computer-readable orcomputer-executable instructions that are executable by at least some ofthe processing elements 518 a-518 n of the multi-processing elementprocessor 518 to perform collecting, caching, and/or transmitting ofdata that is to be stored at the centralized big data appliance 408.

The cache 522 of the centralized big data device CBD may utilize datastorage technology similar to that utilized by the memory 520, or mayutilize different data storage technology, such as a flash memory. In anexample, the cache 522 uses mass or high density data storage technologyto temporarily store data collected by the device CBD prior to thedata's transmission for historization at the centralized big dataappliance 408. The cache 520 may or may not be included in the memory520, and a size of the cache 520 may be selectable or configurable. Thecache 522 is configured to store one or more data entries, and each dataentry includes a value of a datum or data point collected by the deviceCBD, and a respective timestamp or indication of an instance of time atwhich the data value was generated by, created by, received at, orobserved by the device CBD. Both the value of the process control dataand the timestamp stored in each data entry of the cache 522 aretransmitted for storage to the centralized big data appliance 408. In anembodiment, a schema utilized by the cache 522 for data storage at thedevice CBD is included in a schema utilized by the centralized big dataappliance 408.

The device CBD centralized big data collects data that is directlygenerated by and/or directly received at the device CBD, e.g., at therate at which the data is generated or received. For example, the deviceCBD collects all data that is directly generated by and/or directlyreceived at the device CBD at the rate at which the data is generated orreceived. The device CBD may stream at least some of the data inreal-time as the data is generated, created, received or otherwiseobserved by the device CBD, e.g., without using lossy data compressionor any other techniques that may cause loss of original information.Additionally or alternatively, the device CBD may temporarily store atleast some of the collected data in its cache 522, and push at leastsome of the data from its cache 522 when the cache 522 is filled to aparticular threshold or when some other criteria is met.

On the other hand, and referring now to the example block diagram ofFIG. 10C, a distributed process control big data device DBD included inthe process plant 40 of FIG. 9 is configured to collect and locallystore or historize data (e.g., big data) corresponding to a processplant or to a process controlled in the process plant, and does not relyon a centralized big data appliance 408 for data historization. Similarto the centralized distributed big data device CBD, the distributed bigdata device DBD includes a multi-core processor 518 a and a memory 520,and in some cases, an interface 512 to a process control communicationnetwork 515.

However, instead of a cache 522 for temporary big data storage, thedistributed big data device DBD includes a respective embedded big dataapparatus or appliance 530 for long-term big data storage andhistorization, as well as for big data analysis. The embedded big dataappliance 530 includes, for example, an embedded big data storage 532for local storage or historization of data, one or more embedded bigdata receivers 535, one or more embedded big data analyzers 538, and oneor more embedded big data request servicers 540. In an embodiment, theembedded big data receivers 535, the embedded big data analyzers 538,and the embedded big data request servicers 540 comprise respectivecomputer-executable instructions that are stored on a tangible,non-transitory computer readable storage medium (e.g., the embedded bigdata storage 120 or the memory 520), and that are executable by the oneor more processors (e.g., one or more of the cores 518 a-518 n).

The embedded big data storage 532 of the distributed big data device DBDincludes one or more tangible, non-transitory memory storages thatutilize high density memory storage technology, for example, solid statedrive memory, semiconductor memory, optical memory, molecular memory,biological memory, or any other suitable high density memory technology.To the other nodes or devices of the network 400, the embedded big datastorage 532 may have the appearance of a single or unitary logical datastorage area or entity, which may or may not be addressed in the network400 as a different entity from the actual process control big datadevice DBD.

The structure of the embedded big data storage 532 supports thelong-term storage of any and all process control system and plantrelated data collected by the process control distributed big datadevice DBD, in an embodiment. Each entry, data point, or observationstored in the embedded big data storage 532 includes, for example,time-series data such as an indication of the identity of the data(e.g., device, tag, location, etc.), a content of the data (e.g.,measurement, value, etc.), and a timestamp indicating a time at whichthe data was collected, generated, created, received, or observed. Thedata is stored in the embedded big data storage 120 of the device DBDusing a common format including a schema that supports scalable storage,for example, and which may or may not be the same schema as utilized byother distributed big data devices DBD, and which may or may not be thesame schema as utilized by the centralized process control big dataappliance 408.

The embedded big data appliance 530 of the distributed big data deviceDBD includes one or more embedded big data receivers 535, each of whichis configured to receive data collected by the device DBD and cause thecollected data to be stored in the embedded big data storage 532. Thedevice DBD collects data that is directly generated by and/or directlyreceived at the device DBD, e.g., at the rate at which the data isgenerated or received. For example, the device DBD collects all datathat is directly generated by and/or directly received at the device DBDat the rate at which the data is generated or received via theinterfaces 512 and 510.

Additionally, the embedded big data appliance 530 of the distributed bigdata device DBD includes one or more embedded big data analyzers 538,each of which is configured to carry out or perform one or more learninganalyses on data stored in the embedded big data storage 532. At leastsome of the embedded big data analyzers 538 may perform large scale dataanalysis on the stored data (e.g., data mining, data discovery, etc.) todiscover, detect, or learn new information and knowledge based on theaggregated data. For example, data mining generally involves the processof examining large quantities of data to extract or discover new orpreviously unknown interesting patterns such as unusual records ormultiple groups of data records. At least some of the embedded big dataanalyzers 538 may perform large scale data analysis on the stored data(e.g., machine learning analysis, data modeling, pattern recognition,correlation analysis, predictive analysis, etc.) to predict, calculate,or identify implicit relationships or inferences within the stored data.For example, the embedded data analyzers 538 may utilize any number ofdata learning algorithms and classification techniques such as partialleast square (PLS) regression, random forest, and principle componentanalysis (PCA). From the large scale data analysis, the embedded bigdata analyzers 538 of the device DBD may create or generate ensuinglearned data or knowledge, which may be stored in or added to theembedded big data storage 120 of the device DBD. For example, one ormore of the data analyzers 538 performs a data analysis to determine atleast a portion of a process element alignment map (block 290 of FIG.3), to determine upstream process element (block 315) of FIG. 6, todetermine one or more source(s) of behavior variation of a targetprocess element (blocks 318, 320 of FIG. 6, or other data analysesperformed by the methods, systems, apparatuses and techniques describedherein. The embedded big data analyzers 538 may create or generateadditional data that was previously unknown to the host distributed bigdata device DBD. Additionally or alternatively, the embedded big dataanalyzers 538 may create a new or modified application, function,routine, or service based on the results of their data analysis oranalyses.

Furthermore, the embedded big data appliance 532 of the device DBD mayinclude one or more embedded big data request servicers 540, each ofwhich is configured to access localized data stored in the embedded bigdata storage 532, e.g., per the request of a requesting entity orapplication. In an embodiment, at least some of the embedded big datarequest servicers 540 are integral with at least some of the embeddedbig data analyzers 538.

Returning to FIG. 9, as discussed above, the distributed big datadevices DBD and the centralized big data devices CBD may include deviceswhose main function is to automatically generate and/or receive processcontrol data that is used to perform functions to control a process inreal-time in the process plant environment 40, such as processcontrollers, field devices and I/O devices. In a process plantenvironment 40, process controllers receive signals indicative ofprocess measurements made by field devices, process this information toimplement a control routine, and generate control signals that are sentover wired or wireless communication links to other field devices tocontrol the operation of a process in the plant 40. Typically, at leastone field device performs a physical function (e.g., opening or closinga valve, increase or decrease a temperature, etc.) to control theoperation of a process, and some types of field devices may communicatewith controllers using I/O devices. Process controllers, field devices,and I/O devices may be wired or wireless, and any number and combinationof wired and wireless process controllers, field devices and I/O devicesmay be distributed big data nodes DBD and/or centralized big data nodesof the process control big data network 400, each of which locallycollects, analyzes and stores big data.

For example, FIG. 9 illustrates a distributed big data processcontroller device 411 that locally collects, analyzes and stores bigdata of the process control network or plant 40. The controller 411 iscommunicatively connected to wired field devices 415-422 viainput/output (I/O) cards 426 and 428, and is communicatively connectedto wireless field devices 440-446 via a wireless gateway435 and thenetwork backbone 405. (In another embodiment, though, the controller 411may be communicatively connected to the wireless gateway 435 using acommunications network other than the backbone 405, such as by usinganother wired or a wireless communication link.) In FIG. 9, thecontroller 411 is a distributed big data provider node DBD of theprocess control system big data network 400, and is directly connectedto the process control big data network backbone 405.

The controller 411, which may be, by way of example, the DeltaV™controller sold by Emerson Process Management, may operate to implementa batch process or a continuous process using at least some of the fielddevices 415-422 and 440-446. In an embodiment, in addition to beingcommunicatively connected to the process control big data networkbackbone 405, the controller 411 may also be communicatively connectedto at least some of the field devices 415-422 and 440-446 using anydesired hardware and software associated with, for example, standard4-20 mA devices, I/O cards 426, 428, and/or any smart communicationprotocol such as the FOUNDATION® Fieldbus protocol, the HART® protocol,the WirelessHART® protocol, etc. In an embodiment, the controller 411may be communicatively connected with at least some of the field devices415-422 and 440-446 using the big data network backbone 405. In FIG. 9,the controller 411, the field devices 415-422 and the I/O cards 426, 428are wired devices, and the field devices 440-446 are wireless fielddevices. Of course, the wired field devices 415-422 and wireless fielddevices 440-446 could conform to any other desired standard(s) orprotocols, such as any wired or wireless protocols, including anystandards or protocols developed in the future.

The process controller device 411 includes a processor 430 thatimplements or oversees one or more process control routines (e.g., thatare stored in a memory 432), which may include control loops. Theprocessor 430 is configured to communicate with the field devices415-422 and 440-446 and with other nodes that are communicativelyconnected to the backbone 405 (e.g., other distributed big data devicesDBD, centralized big data devices CBD, and/or the centralized big dataappliance 408). It should be noted that any control routines or modules(including quality prediction and fault detection modules or functionblocks) described herein may have parts thereof implemented or executedby different controllers or other devices if so desired. Likewise, thecontrol routines or modules described herein which are to be implementedwithin the process control system 40 may take any form, includingsoftware, firmware, hardware, etc. Control routines may be implementedin any desired software format, such as using object orientedprogramming, ladder logic, sequential function charts, function blockdiagrams, or using any other software programming language or designparadigm. The control routines may be stored in any desired type ofmemory, such as random access memory (RAM), or read only memory (ROM)Likewise, the control routines may be hard-coded into, for example, oneor more EPROMs, EEPROMs, application specific integrated circuits(ASICs), or any other hardware or firmware elements. Thus, thecontroller 411 may be configured to implement a control strategy orcontrol routine in any desired manner.

In some embodiments, the controller 411 implements a control strategyusing what are commonly referred to as function blocks, wherein eachfunction block is an object or other part (e.g., a subroutine) of anoverall control routine and operates in conjunction with other functionblocks (via communications called links) to implement process controlloops within the process control system 40. Control based functionblocks typically perform one of an input function, such as thatassociated with a transmitter, a sensor or other process parametermeasurement device, a control function, such as that associated with acontrol routine that performs PID, fuzzy logic, etc. control, or anoutput function which controls the operation of some device, such as avalve, to perform some physical function within the process controlsystem 40. Of course, hybrid and other types of function blocks exist.Function blocks may be stored in and executed by the controller 411,which is typically the case when these function blocks are used for, orare associated with standard 4-20 ma devices and some types of smartfield devices such as HART devices, or may be stored in and implementedby the field devices themselves, which can be the case with Fieldbusdevices. The controller 411 may include one or more control routines 438that may implement one or more control loops. Each control loop istypically referred to as a control module, and may be performed byexecuting one or more of the function blocks.

Other examples of devices DBD that support distributed big data in theprocess plant or system 40 are the wired field devices 415, and 418-420and the I/O card 426 shown in FIG. 9. As such, at least some of thewired field devices 415, 418-420 and the I/O card 426 may be distributedbig data nodes DBD of the process control system big data network 400.Additionally, FIG. 9 demonstrates that the example process plant 40includes wired centralized big data devices (e.g., wired field devices416, 421 and I/O card 428, as indicated in FIG. 9 by the reference“CBD”) and wired legacy devices (e.g., devices 417 and 422), which mayoperate in conjunction with the wired distributed big data devices 415,418-420, 426 within the process plant. The wired field devices 415-422may be any types of devices, such as sensors, valves, transmitters,positioners, etc., while the I/O cards 426 and 428 may be any types ofI/O devices conforming to any desired communication or controllerprotocol. In FIG. 9, the field devices 415-418 are standard 4-20 mAdevices or HART devices that communicate over analog lines or combinedanalog and digital lines to the I/O card 426, while the field devices419-422 are smart devices, such as FOUNDATION® Fieldbus field devices,that communicate over a digital bus to the I/O card 428 using a Fieldbuscommunications protocol. In some embodiments, though, at least some ofthe big data wired field devices 415, 416 and 418-421 and/or at leastsome of the big data I/O cards 426, 428 additionally or alternativelycommunicate with the controller 411 using the big data network backbone405.

The wireless field devices 440-446 shown in FIG. 9 include examples ofwireless devices DBD that support distributed big data in the processplant or system 40 (e.g., devices 442 a and 42 b). FIG. 9 also includesan example of a wireless centralized big data device (e.g., device 444)as well as an example of a legacy wireless device (e.g., device 446).The wireless field devices 440-446 communicate in a wireless network 470using a wireless protocol, such as the WirelessHART protocol. Suchwireless field devices 440-446 may directly communicate with one or moreother devices or nodes (e.g., distributed big data nodes DBD,centralized big data nodes CBD, or other nodes) of the process controlbig data network 400 that are also configured to communicate wirelessly(using the wireless protocol, for example). To communicate with one ormore other nodes (e.g., distributed big data nodes DBD, centralized bigdata nodes CBD, or other nodes) that are not configured to communicatewirelessly, the wireless field devices 440-446 may utilize a wirelessgateway 435 connected to the backbone 405 or to another process controlcommunications network. Any number of wireless field devices thatsupport distributed big data may be utilized in a process plant 40.

The wireless gateway 435 may be a distributed big data device DBD and/ora centralized big data device CBD that is included in the processcontrol plant or system 40, and provides access to various wirelessdevices 440-458 of a wireless communications network 470. In particular,the wireless gateway 435 provides communicative coupling between thewireless devices 440-458, the wired devices 411-428, and/or other nodesor devices of the process control big data network 400 (including thecontroller 411 of FIG. 9). For example, the wireless gateway 435 mayprovide communicative coupling by using the big data network backbone405 and/or by using one or more other communications networks of theprocess plant 40. The wireless gateway 435 may support distributed bigdata, centralized big data, or both distributed big data and centralizedbig data.

The wireless gateway 435 provides communicative coupling, in some cases,by the routing, buffering, and timing services to lower layers of thewired and wireless protocol stacks (e.g., address conversion, routing,packet segmentation, prioritization, etc.) while tunneling a sharedlayer or layers of the wired and wireless protocol stacks. In othercases, the wireless gateway 435 may translate commands between wired andwireless protocols that do not share any protocol layers. In addition toprotocol and command conversion, the wireless gateway 435 may providesynchronized clocking used by time slots and superframes (sets ofcommunication time slots spaced equally in time) of a scheduling schemeassociated with the wireless protocol implemented in the wirelessnetwork 470. Furthermore, the wireless gateway 435 may provide networkmanagement and administrative functions for the wireless network 470,such as resource management, performance adjustments, network faultmitigation, monitoring traffic, security, and the like.

Similar to the wired field devices 415-422, the wireless field devices440-446 of the wireless network 470 may perform physical controlfunctions within the process plant 40, e.g., opening or closing valvesor take measurements of process parameters. The wireless field devices440-446, however, are configured to communicate using the wirelessprotocol of the network 470. As such, the wireless field devices440-446, the wireless gateway 435, and other wireless nodes 452-458 ofthe wireless network 470 are producers and consumers of wirelesscommunication packets.

In some scenarios, the wireless network 470 may include non-wirelessdevices, which may or may not be big data devices, whether centralizedand/or distributed. For example, a field device 448 of FIG. 9 may be alegacy 4-20 mA device and a field device 450 may be a traditional wiredHART device. To communicate within the network 470, the field devices448 and 450 may be connected to the wireless communications network 470via a wireless adaptor (WA) 452 a or 452 b. In FIG. 9, the wirelessadaptor 452 b is shown as being a legacy wireless adaptor thatcommunicates using the wireless protocol, and the wireless adaptor 452 ais shown as supporting distributed big data and thus is communicativelyconnected to the big data network backbone 405. Additionally, thewireless adaptors 452 a, 452 b may support other communication protocolssuch as Foundation® Fieldbus, PROFIBUS, DeviceNet, etc. Furthermore, thewireless network 470 may include one or more network access points 455a, 455 b, which may be separate physical devices in wired communicationwith the wireless gateway 435 or may be provided with the wirelessgateway 435 as an integral device. In FIG. 9, the network access point455 a is illustrated as being a distributed big data device DBD, whilethe network access point 455 b is a legacy access point. The wirelessnetwork 470 may also include one or more routers 458 to forward packetsfrom one wireless device to another wireless device within the wirelesscommunications network 470, each of which may or may not supportdistributed big data in the process control system 40. The wirelessdevices 440-446 and 452-458 may communicate with each other and with thewireless gateway 435 over wireless links 460 of the wirelesscommunications network 470, and/or via the big data network backbone405, if the wireless devices are distributed and/or centralized big datadevices.

Accordingly, FIG. 9 includes several examples of big data devices DBD,CBD which primarily serve to provide network routing functionality andadministration to various networks of the process control system. Forexample, the wireless gateway 435, the access point 455 a, and therouter458 each include functionality to route wireless packets in thewireless communications network 470. The wireless gateway 435 performstraffic management and administrative functions for the wireless network470, as well as routes traffic to and from wired networks that are incommunicative connection with the wireless network 470. The wirelessnetwork 470 may utilize a wireless process control protocol thatspecifically supports process control messages and functions, such asWirelessHART. As shown in FIG. 9, the devices 435, 455 a, 452 a, 442 a,442 b and 458 of the wireless network 470 support distributed big datain the process control plant 40, however, any number of any types ofnodes of the wireless network 470 may support distributed big data inthe process plant 40.

Other devices that communicate using other wireless protocols may be bigdata devices DBD and/or CBD of the process control big data network 400.In FIG. 9, one or more wireless access points 472 are big data devicesDBD and/or CBD that utilize other wireless protocols, such as WiFi orother IEEE 802.11 compliant wireless local area network protocols,mobile communication protocols such as WiMAX (Worldwide Interoperabilityfor Microwave Access), LTE (Long Term Evolution) or other ITU-R(International Telecommunication Union Radiocommunication Sector)compatible protocols, short-wavelength radio communications such as nearfield communications (NFC) and Bluetooth, or other wirelesscommunication protocols. Typically, such wireless access points 472allow handheld or other portable computing devices (e.g., user interfacedevices) to communicative over a respective wireless network that isdifferent from the wireless network 470 and that supports a differentwireless protocol than the wireless network 470. In some scenarios, inaddition to portable computing devices, one or more process controldevices (e.g., controller 411, field devices 415-422, or wirelessdevices 435, 440-458) may also communicate using the wireless protocolsupported by the access points 472.

Additionally in FIG. 9, one or more gateways 475, 748 to systems thatare external to the immediate process control system 40 are big datadevices DBD and/or CBD of the process control big data network 400.Typically, such systems are customers or suppliers of informationgenerated or operated on by the process control system 40. For example,a plant gateway node 475 may communicatively connect the immediateprocess plant 40 (having its own respective process control big datanetwork backbone 405) with another process plant having its ownrespective process control big data network backbone. In an embodiment,a single process control big data network backbone 405 may servicemultiple process plants or process control environments.

In FIG. 9, a plant gateway node 475 communicatively connects theimmediate process plant 40 to a legacy or prior art process plant thatdoes not include a process control big data network 400 or backbone 405.In this example, the plant gateway node 475 may convert or translatemessages between a protocol utilized by the process control big databackbone 405 of the plant 40 and a different protocol utilized by thelegacy system (e.g., Ethernet, Profibus, Fieldbus, DeviceNet, etc.). Theone or more external system gateway nodes 478 communicatively connectthe process control big data network 400 with the network of an externalpublic or private system, such as a laboratory system (e.g., LaboratoryInformation Management System or LIMS), an operator rounds database, amaterials handling system, a maintenance management system, a productinventory control system, a production scheduling system, a weather datasystem, a shipping and handling system, a packaging system, theInternet, another provider's process control system, or other externalsystems.

Although FIG. 9 only illustrates a single controller 411 with a finitenumber of field devices 415-22 and 440-446, this is only an illustrativeand non-limiting embodiment. Any number of controllers 411 may supportdistributed and/or centralized big data, and any of the controllers 411may communicate with any number of wired or wireless field devices415-422, 440-446 to control a process in the plant 40. Furthermore, theprocess plant 40 may also include any number of wireless gateways 435,routers 458, access points 455, wireless process control communicationsnetworks 470, access points 472, and/or gateways 475, 478. Stillfurther, FIG. 9 may include any number of centralized big dataappliances 408, which may receive and store collected data and/orgenerated learned data or knowledge from any or all of the devices inthe process plant 40.

Further, the combination of aspects, devices, and components included inthe example process plant 40 as illustrated by FIG. 9 (e.g., distributedbig data devices DBD, centralized big data devices CBD, centralized bigdata appliance 408, wired devices, wireless devices, legacy devices,gateways, access points, etc.) is exemplary only. The techniques,systems, methods, and apparatuses disclosed herein may be utilized inprocess plants with zero or more any of the aspects illustrated in FIG.9. For example, the techniques, systems, methods, and apparatusesdisclosed herein may be utilized in a process plant without acentralized big data appliance 408, or with only centralized big datadevices CBD and no distributed big data devices DBD. In another example,the techniques, systems, methods, and apparatuses disclosed herein maybe utilized in a process plant with only legacy devices.

Embodiments of the techniques described in the present disclosure mayinclude any number of the following aspects, either alone orcombination:

1. A method for determining sources of variations of behaviors ofprocess elements used in a process plant to control a process. Themethod includes receiving an indication of a target process elementincluded in the plurality of process elements; defining, based on aplurality of diagrams of the process or of the process plant, at least aportion of a process element alignment map corresponding to a pluralityof process elements used in the process plant to control the process;and determining, based on the at least the portion of the processelement alignment map, an upstream set of process elements correspondingto the target process element. The method also includes providingindications of the upstream set of process elements to a data analysisto determine a respective strength of an impact of each upstream processelement on a behavior of the target process element, where a set ofinputs to the data analysis includes the indications of the upstream setof process elements and excludes any user-generated input. Additionally,the method includes determining, based on the respective strengths ofimpacts of the upstream set of process elements, at least a subset ofthe upstream set of process elements to be one or more sources of avariation in the behavior of the target process element; and causing anindication of the one or more sources of the variation in the behaviorof the target process element to be provided to a recipient application,where the recipient application is a user interface application oranother application. At least part of the method may be performed by oneor more computing devices.

2. The method of the previous aspect, wherein defining the at least theportion of the process element alignment map of the process comprisesdefining at least a portion of a process element alignment map thatincludes, for each process element included in the plurality of processelements, a respective identifier of the each process element and anindication of a respective order of an occurrence of a respective eventat the each process element to control the process relative to anoccurrence of a respective event at at least one other process elementto control the process. Additionally, the plurality of process elementsincludes a plurality of devices, a plurality of process variables, and aplurality of measurements.

3. The method of any one of the previous aspects, wherein defining theat least the portion of the process element alignment map includesextracting or obtaining a set of data from the plurality of diagrams.The extracted set of data includes, for each process element of at leasta portion of the plurality of process elements, the respectiveidentifier of the each process element and an indication of a respectivephysical location of the each process element in the process plant.Further, the method includes generating, based on the extracted orobtained set of data, the respective order of the each process elementincluded in the at least the portion of the plurality of processelements.

4. The method of any one of the previous aspects, wherein extracting orobtaining the set of data from the plurality of diagrams comprisesextracting the set of data from at least two or more of: a Piping andInstrumentation Diagram (PI&D), a Process Flow Diagram (PFD), a LoopDiagram, a display view, another diagram of the process plant, streameddata, or user input.

5. The method of any one of the previous aspects, further comprisingreceiving additional data via a user interface; and wherein defining theat the at least the portion of the process element alignment map isbased on the extracted or obtained data and on the additional data.

6. The method of any one of the previous aspects, wherein determiningthe upstream set of process elements corresponding to the target processelement comprises determining a subset of the plurality of processelements, where each process element of the subset has a respectiveorder in the process element alignment map ahead of or adjacent to therespective order of the target process element in the process elementalignment map.

7. The method of any one of the previous aspects, wherein receiving theindication of the target process element occurs prior to defining the atleast the portion of the process element alignment map, and the methodfurther comprises determining a subset of the plurality of processelements associated with the target process element. Additionally,defining the at least the portion of the process element alignment mapcomprises determining only a part of the process element alignment map,where the part of the process element alignment map corresponds to thedetermined subset of the plurality of process elements associated withthe target process element.

8. The method of any one of the previous aspects, wherein receiving theindication of the target process element occurs after defining the atleast the portion of the process element alignment map, and wherein thetarget process element is included in the at least the portion of theprocess element alignment map.

9. The method of any one of the previous aspects, wherein providing theindications of the upstream set of process elements to the data analysiscomprises providing the indications of the upstream set of processelements to at least one of: a principal component analysis (PCA), across correlation analysis, a partial least squares regression analysis(PLS), or another predictive data analysis function.

10. The method of any one of the previous aspects, further comprising,determining, based on the at least the portion of the process elementalignment map, a respective impact delay of the each upstream processelement with respect to the target process element. The respectiveimpact delay of the each upstream process element corresponds to therespective strength of impact of the each upstream process element and atime offset between the each upstream process element and the targetprocess element. Further, determining the one or more sources of thevariation in the behavior of the target process element based on therespective strengths of impacts of the upstream process elementscomprises determining the one or more sources of variation in thebehavior or the target process element based on the respective impactdelays of the upstream process elements.

11. The method of any one of the previous aspects, wherein determiningthe at least the subset of the upstream set of process elements to bethe one or more sources of the variation of the behavior of the targetprocess element is based on at least one of (a) a thresholdcorresponding to an ordering of the upstream set of process elements, or(b) a threshold corresponding to the respective strengths of impacts ofthe upstream set of process elements.

12. The method of any one of the previous aspects, further comprisingstoring an identifier of the variation in the behavior of the targetprocess element in conjunction with one or more identifiers of thedetermined one or more sources of the variation in the behavior of thetarget process element.

13. The method of any one of the previous aspects, wherein causing theindication of the one or more sources to be provided to the recipientapplication comprises causing the indication of the one or more sourcesto be automatically provided to the recipient application less than onesecond after receiving the indication of the target process element.

14. The method of any one of the previous aspects, wherein at least oneof defining the at least the portion of the process element alignmentmap or determining the at least the subset of the upstream set ofprocess elements to be the one or more sources of the variation in thebehavior of the target process element is performed by a serviceapplication executing on the one or more computing devices.

15. The method of any one of the previous aspects, wherein the serviceapplication is hosted by a big data appliance for the process plant.

16. The method of any one of the previous aspects, wherein the executingservice application is an instance of a service application hosted on ahosting device included in the process plant, and wherein one of: thehosting device is the target process element; or the hosting device isconfigured to, during run-time of the process, receive data originatedby the target process element and automatically operate on the receiveddata to control the process.

17. The method of any one of the previous aspects, wherein at least aportion of the method is performed in a background of the one or morecomputing devices.

18. The method of claim 1, wherein receiving the indication of thetarget process element comprises receiving the indication of the targetprocess element from an unsupervised application. The unsupervisedapplication may be executing at one or more computing devices at whichthe method is being executed, or may be executing at another one or morecomputing devices. Further, the unsupervised application is at least oneof an unsupervised discovery, learning, training, or analyticsapplication.

19. An apparatus for automatically determining a process elementalignment map of a plurality of process elements used to control atleast a portion of a process in a process plant. The apparatus may beconfigured to perform zero or more of any of the previous methods.Additionally, the apparatus includes one or more tangible,non-transitory, computer-readable storage media storingcomputer-executable instructions that, when executed by one or moreprocessors, cause the apparatus to obtain a set of data from a pluralityof data sources storing data descriptive of the plurality of the processelements. The obtained set of data includes, for each of the pluralityof process elements, a respective identification of the each processelement and an indication of a respective physical location of the eachprocess element in the process plant. Further, the plurality of processelements includes a plurality of devices, a plurality of processvariables, and a plurality of measurements used to control the process.The computer-executable instructions are also executable to determine,based on the obtained set of data, the process element alignment map,where the process element alignment map indicates, for each processelement included in the plurality of process elements, the respectiveidentification of the each process element and an indication of arespective order of an occurrence of a respective event at the eachprocess element to control the process relative to an occurrence of arespective event at at least one other process element to control theprocess.

20. The apparatus of any one of the previous aspects, wherein theplurality of data sources includes at least two of a Piping andInstrumentation Diagram (PI&D), a Process Flow Diagram (PFD), a LoopDiagram, another diagram of the process plant, streamed data, or amanually generated data source.

21. The apparatus of any one of the previous aspects, wherein theplurality of data sources includes at least two different operatordisplay views, and wherein each of the two different operator displayviews is configured for at least one of the process or the processplant.

22. The apparatus of any one of the previous aspects, wherein: a firstdata source of the plurality of data sources stores data descriptive ofa first process element of the plurality of process elements andexcludes any data descriptive of a second process element of theplurality of process elements; a second data source of the plurality ofdata sources stores data descriptive of the second process element andexcludes any data descriptive of the first process element; and thecomputer-executable instructions are executable to cause the apparatusto determine the respective order corresponding to the first processelement in the process element alignment map with respect to therespective order corresponding to the second process element in theprocess element alignment map.

23. The apparatus of any one of the previous aspects, wherein: a firstdata source of the plurality of data sources stores first datadescriptive of a specific process element of the plurality of processelements and excludes second data descriptive of the specific processelement; a second data source of the plurality of data sources storesthe second data descriptive of the specific process element and excludesthe first data descriptive of the specific process element; and thecomputer-executable instructions are executable to cause the apparatusto determine the respective order corresponding to the specific processelement in the process element alignment map based on the first data andthe second data.

24. The apparatus any one of the previous aspects, wherein: each datasource of the plurality of data sources stores a respective subset ofthe obtained set of data, and the computer-executable instructions arefurther executable to cause the apparatus to determine the respectivesubsets of the obtained set of data.

25. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are further executable to cause theapparatus to: receive additional data via a user interface; anddetermine the process element alignment map based on the obtained set ofdata and the additional data.

26. The apparatus of any one of the previous aspects, wherein theadditional data is received as a response to a presentation of a draftof the process element alignment map at the user interface.

27. The apparatus of any one of the previous aspects, wherein theprocess alignment map further includes metadata for at least some of theprocess elements included in the plurality of process elements.

28. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are further executable to cause theapparatus to store the process element alignment map.

29. The apparatus of any one of the previous aspects, wherein theprocess element alignment map is stored at a big data appliance of theprocess plant, and wherein the big data appliance is configured toprovide access to contents of the process element alignment map.

30. The apparatus of any one of the previous aspects, wherein theprocess element alignment map is stored at a field device configured tooperate during run-time to control the process in the process plant.

31. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are executed by one or more processorsof a big data appliance of the process plant.

32. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are executed by one or more processorsof a field device operating during run-time to control the process inthe process plant.

33. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are further executable to cause theapparatus to cause an indication of the process element alignment map tobe provided to one or more recipient applications, where the one or morerecipient applications include at least one of a user interfaceapplication or another application executing on one or more computingdevices.

34. The apparatus of any one of the previous aspects, wherein the one ormore recipient applications include at least one of a discovery,learning, training, or analytics application.

35. The apparatus of any one of the previous aspects, wherein theanother application is an unsupervised application.

36. The apparatus of any one of the previous aspects, wherein: aninitiation of an execution of the computer-executable instructions isbased on a request to determine the process element alignment map, andthe indication of the process element alignment map is provided to theone or more recipient applications, in real-time, as a response to therequest.

37. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are further executable to cause theapparatus to present an indication of the process element alignment map,in at least one of a table format or a graph format, on a userinterface.

38. An apparatus for automatically determining sources of a variation ofa behavior of a target process element used in a process plant tocontrol a process. The apparatus may include zero or more of theprevious aspects, and the apparatus comprises one or more tangible,non-transitory, computer-readable storage media storingcomputer-executable instructions that, when executed by one or moreprocessors, cause the apparatus to receive an indication of the targetprocess element. The target process element is included in a pluralityof process elements used in the process plant to control the process,and the plurality of process elements includes a plurality of devices, aplurality of process variables, and a plurality of measurements. Thecomputer-executable instructions are further executable to determine,using at least a portion of a process element alignment map, a subset ofthe plurality of process elements that are upstream of the targetprocess element, where the process alignment map indicates, for eachprocess element included in the plurality of process elements, arespective identifier of the each process element and an indication of arespective order of an occurrence of a respective event at the eachprocess element to control the process relative to an occurrence of arespective event at at least one other process element to control theprocess. The subset of the plurality of process elements that areupstream of the target process element is a set of upstream processelements, and each upstream process element has a respective order inthe process element alignment map that is ahead of or adjacent to therespective order of the target process element in the process elementalignment map. The computer-executable instructions are still furtherexecutable to cause the set of upstream process elements to be used in adata analysis to determine, for each upstream process element, arespective impact delay, where the respective impact delay correspondsto a time offset from a time at which a particular event occurs at theeach upstream process element to a time at which a change in thebehavior of the target process element resulting from the occurrence ofthe particular event at the each upstream process element occurs.Additionally, the computer-executable instructions are executable tocause at least a portion of the set of upstream process elements to beindicated, to a recipient application, as one or more sources of thevariation of the behavior of the target process element, where the atleast a portion of the set of upstream process elements is determinedbased on the respective impact delays of the upstream process elements.

39. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions further cause the apparatus to preventa process element of the plurality of process elements that is notupstream of the target process element from being used in the dataanalysis.

40. The apparatus of any one of the previous aspects, wherein the dataanalysis determines, for the each upstream process element and based onthe respective impact delay of the each upstream process element, arespective strength of an impact of the each upstream process element onthe variation in the behavior of the target process element; and whereinthe computer-executable instructions cause the apparatus to determinethe at least the portion of the plurality of process elements as the oneor more sources of the variation of the behavior of the target processelement based on the respective strengths of impacts of the upstreamprocess elements.

41. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions cause the apparatus to determine the atleast the portion of the plurality of process elements as the one ormore sources of the variation of the behavior of the target processelement further based on a threshold.

42. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions further cause the apparatus to causeindications of the respective strengths of impact corresponding to theat least the portion of the set of upstream process elements to beindicated to the recipient application.

43. The apparatus of any one of the previous aspects, wherein the dataanalysis is a predictive data analysis comprising at least one of: aprincipal component analysis (PCA), a cross correlation analysis, apartial least squares regression analysis (PLS), or other predictiveanalysis technique.

44. The apparatus of any one of the previous aspects, wherein thepredictive data analysis further determines, for the each upstreamprocess element, a respective strength of an impact of the each upstreamprocess element on the variation in the behavior of the target processelement; and wherein the respective impact delay of the each upstreamprocess element is determined based on the respective strength of impactof the each upstream process element.

45. The apparatus of any one of the previous aspects, wherein at least asubset of the respective strengths of impact of the upstream processelements are included in a model generated by the data analysis; thetime offset is a particular time offset included in a plurality of timeoffsets corresponding to the target process element; the time offset isdetermined based on the model; and the respective impact delay of theeach upstream process element is determined based on the particular timeoffset.

46. The apparatus of any one of the previous aspects, wherein theprocess element alignment map is defined from a set of data obtainedfrom a plurality of data sources descriptive of the plurality of processelements; the plurality of data sources includes, for each processelement, a respective identification of the each process element and anindication of a respective physical location of the each process elementin the process plant; and the time offset of the each upstream processelement is determined based on the respective physical location of theeach upstream process element.

47. The apparatus of any one of the previous aspects, wherein theplurality of data sources includes at least two diagrams selected from:a set of Piping and Instrumentation Diagrams (PI&Ds), a set of ProcessFlow Diagrams (PFDs), a set of Loop Diagrams, a set of operator displayviews configured for the process or for the process plant, or a set ofother diagrams of the process plant.

48. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are further executable to cause theapparatus to determine the at least the portion of the process elementalignment map based on the received indication of the target processelement.

49. The apparatus of any one of the previous aspects, wherein theindication of the at least the portion of the set of upstream processelements is provided to the recipient application as a real-timeresponse to the reception of the indication of the target processelement.

50. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are included in an unsupervisedapplication, where the unsupervised application is at least one of adiscovery, learning, training, or analytics application.

51. The apparatus of any one of the previous aspects, wherein theindication of the target process element is received via an applicationthat is at least one of: a user interface application; an unsupervisedapplication executing, in a run-time environment of the process plant,on real-time data generated by the process plant; a discoveryapplication; a learning application; a training application; ananalytics application; an application operating in a background of oneor more computing devices on which the computer-executable instructionsare being executed; or an application executing on one or more computingdevices different from one or more computing devices on which thecomputer-executable instructions are being executed.

52. The apparatus any one of the previous aspects, wherein the recipientapplication is at least one of: a user interface application; anunsupervised application executing, in a run-time environment of theprocess plant, on real-time data generated by the process plant; adiscovery application; a learning application; a training application;an analytics application; an application operating in a background ofone or more computing devices on which the computer-executableinstructions are being executed; or an application executing on one ormore computing devices different from one or more computing devices onwhich the computer-executable instructions are being executed.

53. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are executed by one or more processorsof a big data appliance of the process plant.

54. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are executed by one or more processorsof a field device operating in real-time to control the process in theprocess plant.

55. The apparatus of any one of the previous aspects, wherein thecomputer-executable instructions are further executable to store anidentifier of the variation of the behavior of the target processelement and of an indication of the at least one or more sources of thevariation of the behavior of the target process element.

56. The apparatus of any one of the previous aspects, wherein theidentifier of the variation of the behavior of the target processelement and of the indication of the at least one or more sources isprovided by a user.

57. Any one of the preceding aspects in combination with any one or moreother preceding aspects.

When implemented in software, any of the applications, services, andengines described herein may be stored in any tangible, non-transitorycomputer readable memory such as on a magnetic disk, a laser disk, solidstate memory device, molecular memory storage device, or other storagemedium, in a RAM or ROM of a computer or processor, etc. Although theexample systems disclosed herein are disclosed as including, among othercomponents, software and/or firmware executed on hardware, it should benoted that such systems are merely illustrative and should not beconsidered as limiting. For example, it is contemplated that any or allof these hardware, software, and firmware components could be embodiedexclusively in hardware, exclusively in software, or in any combinationof hardware and software. Accordingly, while the example systemsdescribed herein are described as being implemented in software executedon a processor of one or more computer devices, persons of ordinaryskill in the art will readily appreciate that the examples provided arenot the only way to implement such systems.

Thus, while the present invention has been described with reference tospecific examples, which are intended to be illustrative only and not tobe limiting of the invention, it will be apparent to those of ordinaryskill in the art that changes, additions or deletions may be made to thedisclosed embodiments without departing from the spirit and scope of theinvention.

What is claimed:
 1. A method for determining sources of variations ofbehaviors of process elements used in a process plant to control aprocess, the method comprising: receiving, at the one or more computingdevices, an indication of a target process element included in theplurality of process elements; defining, by one or more computingdevices and based on a plurality of diagrams of the process or of theprocess plant, at least a portion of a process element alignment mapcorresponding to a plurality of process elements used in the processplant to control the process; determining, by the one or more computingdevices and based on the at least the portion of the process elementalignment map, an upstream set of process elements corresponding to thetarget process element; providing, by the one or more computing devices,indications of the upstream set of process elements to a data analysisto determine a respective strength of an impact of each upstream processelement on a behavior of the target process element, wherein a set ofinputs to the data analysis includes the indications of the upstream setof process elements and excludes any user-generated input; determining,by the one or more computing devices and based on the respectivestrengths of impacts of the upstream set of process elements, at least asubset of the upstream set of process elements to be one or more sourcesof a variation in the behavior of the target process element; andcausing, by the one or more computing devices, an indication of the oneor more sources of the variation in the behavior of the target processelement to be provided to a recipient application, the recipientapplication being a user interface application or another application.2. The method of claim 1, wherein: defining the at least the portion ofthe process element alignment map of the process comprises defining atleast a portion of a process element alignment map that includes, foreach process element included in the plurality of process elements, arespective identifier of the each process element and an indication of arespective order of an occurrence of a respective event at the eachprocess element to control the process relative to an occurrence of arespective event at at least one other process element to control theprocess; and the plurality of process elements including a plurality ofdevices, a plurality of process variables, and a plurality ofmeasurements.
 3. The method of claim 2, wherein defining, by the one ofmore computing devices, the at least the portion of the process elementalignment map comprises: extracting, by the one or more computingdevices, a set of data from the plurality of diagrams, the extracted setof data including, for each process element included in at least aportion of the plurality of process elements, the respective identifierof the each process element and an indication of a respective physicallocation of the each process element in the process plant; andgenerating, based on the extracted set of data, the respective order ofthe each process element included in the at least the portion of theplurality of process elements.
 4. The method of claim 3, whereinextracting the set of data from the plurality of diagrams comprisesextracting the set of data from at least two or more of: a Piping andInstrumentation Diagram (PI&D), a Process Flow Diagram (PFD), a LoopDiagram, a display view, another diagram of the process plant, streameddata, or user input.
 5. The method of claim 3, further comprisingreceiving additional data via a user interface; and wherein defining theat the at least the portion of the process element alignment map isbased on the extracted data and on the additional data.
 6. The method ofclaim 1, wherein determining the upstream set of process elementscorresponding to the target process element comprises determining asubset of the plurality of process elements, each process element ofwhich has a respective order in the process element alignment map aheadof or adjacent to the respective order of the target process element inthe process element alignment map.
 7. The method of claim 1, wherein:receiving the indication of the target process element occurs prior todefining the at least the portion of the process element alignment map;the method further comprises determining a subset of the plurality ofprocess elements associated with the target process element; anddefining the at least the portion of the process element alignment mapcomprises determining only a part of the process element alignment map,wherein the part of the process element alignment map corresponds to thedetermined subset of the plurality of process elements associated withthe target process element.
 8. The method of claim 1, wherein receivingthe indication of the target process element occurs after defining theat least the portion of the process element alignment map, and whereinthe target process element is included in the at least the portion ofthe process element alignment map.
 9. The method of claim 1, whereinproviding the indications of the upstream set of process elements to thedata analysis comprises providing the indications of the upstream set ofprocess elements to at least one of: a principal component analysis(PCA), a cross correlation analysis, a partial least squares regressionanalysis (PLS), or another predictive data analysis function.
 10. Themethod of claim 1, further comprising, determining, by the one or morecomputing devices and based on the at least the portion of the processelement alignment map, a respective impact delay of the each upstreamprocess element with respect to the target process element, therespective impact delay of the each upstream process elementcorresponding to the respective strength of impact of the each upstreamprocess element and a time offset between the each upstream processelement and the target process element; and wherein determining the oneor more sources of the variation in the behavior of the target processelement based on the respective strengths of impacts of the upstreamprocess elements comprises determining the one or more sources ofvariation in the behavior or the target process element based on therespective impact delays of the upstream process elements.
 11. Themethod of claim 1, wherein determining the at least the subset of theupstream set of process elements to be the one or more sources of thevariation of the behavior of the target process element is based on atleast one of (a) a threshold corresponding to an ordering of theupstream set of process elements, or (b) a threshold corresponding tothe respective strengths of impacts of the upstream set of processelements.
 12. The method of claim 1, further comprising storing anidentifier of the variation in the behavior of the target processelement in conjunction with one or more identifiers of the determinedone or more sources of the variation in the behavior of the targetprocess element.
 13. The method of claim 1, wherein causing theindication of the one or more sources to be provided to the recipientapplication comprises causing the indication of the one or more sourcesto be automatically provided to the recipient application less than onesecond after receiving the indication of the target process element. 14.The method of claim 1, wherein at least one of defining the at least theportion of the process element alignment map or determining the at leastthe subset of the upstream set of process elements to be the one or moresources of the variation in the behavior of the target process elementis performed by a service application executing on the one or morecomputing devices.
 15. The method of claim 14, wherein the serviceapplication is hosted by a big data appliance for the process plant. 16.The method of claim 14, wherein the executing service application is aninstance of a service application hosted on a hosting device included inthe process plant, and wherein one of: the hosting device is the targetprocess element; or the hosting device is configured to, during run-timeof the process, receive data originated by the target process elementand automatically operate on the received data to control the process.17. The method of claim 1, wherein at least a portion of the method isperformed in a background of the one or more computing devices.
 18. Themethod of claim 1, wherein receiving the indication of the targetprocess element comprises receiving the indication of the target processelement from an unsupervised application executing at the one or morecomputing devices or executing at another one or more computing devices,the unsupervised application being at least one of an unsuperviseddiscovery, learning, training, or analytics application.
 19. Anapparatus for automatically determining a process element alignment mapof a plurality of process elements used to control at least a portion ofa process in a process plant, the apparatus comprising: one or moretangible, non-transitory, computer-readable storage media storingcomputer-executable instructions that, when executed by one or moreprocessors, cause the apparatus to: obtain a set of data from aplurality of data sources storing data descriptive of the plurality ofthe process elements, the obtained set of data including, for each ofthe plurality of process elements, a respective identification of theeach process element and an indication of a respective physical locationof the each process element in the process plant, and the plurality ofprocess elements including a plurality of devices, a plurality ofprocess variables, and a plurality of measurements used to control theprocess; and determine, based on the obtained set of data, the processelement alignment map, the process element alignment map indicating, foreach process element included in the plurality of process elements, therespective identification of the each process element and an indicationof a respective order of an occurrence of a respective event at the eachprocess element to control the process relative to an occurrence of arespective event at at least one other process element to control theprocess.
 20. The apparatus of claim 19, wherein the plurality of datasources includes at least two of a Piping and Instrumentation Diagram(PI&D), a Process Flow Diagram (PFD), a Loop Diagram, another diagram ofthe process plant, streamed data, or a manually generated data source.21. The apparatus of claim 19, wherein the plurality of data sourcesincludes at least two different operator display views, and wherein eachof the two different operator display views is configured for at leastone of the process or the process plant.
 22. The apparatus of claim 19,wherein: a first data source of the plurality of data sources storesdata descriptive of a first process element of the plurality of processelements and excludes any data descriptive of a second process elementof the plurality of process elements; a second data source of theplurality of data sources stores data descriptive of the second processelement and excludes any data descriptive of the first process element;and the computer-executable instructions are executable to cause theapparatus to determine the respective order corresponding to the firstprocess element in the process element alignment map with respect to therespective order corresponding to the second process element in theprocess element alignment map.
 23. The apparatus of claim 19, wherein: afirst data source of the plurality of data sources stores first datadescriptive of a specific process element of the plurality of processelements and excludes second data descriptive of the specific processelement; a second data source of the plurality of data sources storesthe second data descriptive of the specific process element and excludesthe first data descriptive of the specific process element; and thecomputer-executable instructions are executable to cause the apparatusto determine the respective order corresponding to the specific processelement in the process element alignment map based on the first data andthe second data.
 24. The apparatus of claim 19, wherein: each datasource of the plurality of data sources stores a respective subset ofthe obtained set of data, and the computer-executable instructions arefurther executable to cause the apparatus to determine the respectivesubsets of the obtained set of data.
 25. The apparatus of claim 19,wherein the computer-executable instructions are further executable tocause the apparatus to: receive additional data via a user interface;and determine the process element alignment map based on the obtainedset of data and the additional data.
 26. The apparatus of claim 25,wherein the additional data is received as a response to a presentationof a draft of the process element alignment map at the user interface.27. The apparatus of claim 19, wherein the process alignment map furtherincludes metadata for at least some of the process elements included inthe plurality of process elements.
 28. The apparatus of claim 19,wherein the computer-executable instructions are further executable tocause the apparatus to store the process element alignment map.
 29. Theapparatus of claim 28, wherein the process element alignment map isstored at a big data appliance of the process plant, and wherein the bigdata appliance is configured to provide access to contents of theprocess element alignment map.
 30. The apparatus of claim 28, whereinthe process element alignment map is stored at a field device configuredto operate during run-time to control the process in the process plant.31. The apparatus of claim 19, wherein the computer-executableinstructions are executed by one or more processors of a big dataappliance of the process plant.
 32. The apparatus of claim 19, whereinthe computer-executable instructions are executed by one or moreprocessors of a field device operating during run-time to control theprocess in the process plant.
 33. The apparatus of claim 19, wherein thecomputer-executable instructions are further executable to cause theapparatus to cause an indication of the process element alignment map tobe provided to one or more recipient applications, the one or morerecipient applications including at least one of a user interfaceapplication or another application executing on one or more computingdevices.
 34. The apparatus of claim 33, wherein the one or morerecipient applications include at least one of a discovery, learning,training, or analytics application.
 35. The apparatus of claim 33,wherein the another application is an unsupervised application.
 36. Theapparatus of claim 33, wherein: an initiation of an execution of thecomputer-executable instructions is based on a request to determine theprocess element alignment map, and the indication of the process elementalignment map is provided to the one or more recipient applications, inreal-time, as a response to the request.
 37. The apparatus of claim 19,wherein the computer-executable instructions are further executable tocause the apparatus to present an indication of the process elementalignment map, in at least one of a table format or a graph format, on auser interface.
 38. An apparatus for automatically determining sourcesof a variation of a behavior of a target process element used in aprocess plant to control a process, the apparatus comprising: one ormore tangible, non-transitory, computer-readable storage media storingcomputer-executable instructions that, when executed by one or moreprocessors, cause the apparatus to: receive an indication of the targetprocess element, the target process element included in a plurality ofprocess elements used in the process plant to control the process, andthe plurality of process elements including a plurality of devices, aplurality of process variables, and a plurality of measurements;determine, using at least a portion of a process element alignment map,a subset of the plurality of process elements that are upstream of thetarget process element, the process alignment map indicating, for eachprocess element included in the plurality of process elements, arespective identifier of the each process element and an indication of arespective order of an occurrence of a respective event at the eachprocess element to control the process relative to an occurrence of arespective event at at least one other process element to control theprocess, the subset of the plurality of process elements that areupstream of the target process element being a set of upstream processelements, and each upstream process element having a respective order inthe process element alignment map that is ahead of or adjacent to therespective order of the target process element in the process elementalignment map; cause the set of upstream process elements to be used ina data analysis to determine, for each upstream process element, arespective impact delay, the respective impact delay corresponding to atime offset from a time at which a particular event occurs at the eachupstream process element to a time at which a change in the behavior ofthe target process element resulting from the occurrence of theparticular event at the each upstream process element occurs; and causeat least a portion of the set of upstream process elements to beindicated, to a recipient application, as one or more sources of thevariation of the behavior of the target process element, the at least aportion of the set of upstream process elements determined based on therespective impact delays of the upstream process elements.
 39. Theapparatus of claim 38, wherein the computer-executable instructionsfurther cause the apparatus to prevent a process element of theplurality of process elements that is not upstream of the target processelement from being used in the data analysis.
 40. The apparatus of claim38, wherein: the data analysis determines, for the each upstream processelement and based on the respective impact delay of the each upstreamprocess element, a respective strength of an impact of the each upstreamprocess element on the variation in the behavior of the target processelement; and the computer-executable instructions cause the apparatus todetermine the at least the portion of the plurality of process elementsas the one or more sources of the variation of the behavior of thetarget process element based on the respective strengths of impacts ofthe upstream process elements.
 41. The apparatus of claim 38, whereinthe computer-executable instructions cause the apparatus to determinethe at least the portion of the plurality of process elements as the oneor more sources of the variation of the behavior of the target processelement further based on a threshold.
 42. The apparatus of claim 38,wherein the computer-executable instructions further cause the apparatusto cause indications of the respective strengths of impact correspondingto the at least the portion of the set of upstream process elements tobe indicated to the recipient application.
 43. The apparatus of claim38, wherein the data analysis is a predictive data analysis comprisingat least one of: a principal component analysis (PCA), a crosscorrelation analysis, a partial least squares regression analysis (PLS),or other predictive analysis technique.
 44. The apparatus of claim 43,wherein: the predictive data analysis further determines, for the eachupstream process element, a respective strength of an impact of the eachupstream process element on the variation in the behavior of the targetprocess element; and the respective impact delay of the each upstreamprocess element is determined based on the respective strength of impactof the each upstream process element.
 45. The apparatus of claim 44,wherein: at least a subset of the respective strengths of impact of theupstream process elements are included in a model generated by the dataanalysis; the time offset is a particular time offset included in aplurality of time offsets corresponding to the target process element;the time offset is determined based on the model; and the respectiveimpact delay of the each upstream process element is determined based onthe particular time offset.
 46. The apparatus of claim 38, wherein: theprocess element alignment map is defined from a set of data obtainedfrom a plurality of data sources descriptive of the plurality of processelements; the plurality of data sources includes, for each processelement, a respective identification of the each process element and anindication of a respective physical location of the each process elementin the process plant; and the time offset of the each upstream processelement is determined based on the respective physical location of theeach upstream process element.
 47. The apparatus of claim 46, whereinthe plurality of data sources includes at least two diagrams selectedfrom: a set of Piping and Instrumentation Diagrams (PI&Ds), a set ofProcess Flow Diagrams (PFDs), a set of Loop Diagrams, a set of operatordisplay views configured for the process or for the process plant, or aset of other diagrams of the process plant.
 48. The apparatus of claim38, wherein the computer-executable instructions are further executableto cause the apparatus to determine the at least the portion of theprocess element alignment map based on the received indication of thetarget process element.
 49. The apparatus of claim 38, wherein theindication of the at least the portion of the set of upstream processelements is provided to the recipient application as a real-timeresponse to the reception of the indication of the target processelement.
 50. The apparatus of claim 38, wherein the computer-executableinstructions are included in an unsupervised application, theunsupervised application being at least one of a discovery, learning,training, or analytics application.
 51. The apparatus of claim 38,wherein the indication of the target process element is received via anapplication that is at least one of: a user interface application; anunsupervised application executing, in a run-time environment of theprocess plant, on real-time data generated by the process plant; adiscovery application; a learning application; a training application;an analytics application; an application operating in a background ofone or more computing devices; or an application executing on one ormore computing devices different from one or more computing devices onwhich the computer-executable instructions are executed.
 52. Theapparatus of claim 38, wherein the recipient application is at least oneof: a user interface application; an unsupervised application executing,in a run-time environment of the process plant, on real-time datagenerated by the process plant; a discovery application; a learningapplication; a training application; an analytics application; anapplication operating in a background of one or more computing devices;or an application executing on one or more computing devices differentfrom one or more computing devices on which the computer-executableinstructions are executed.
 53. The apparatus of claim 38, wherein thecomputer-executable instructions are executed by one or more processorsof a big data appliance of the process plant.
 54. The apparatus of claim38, wherein the computer-executable instructions are executed by one ormore processors of a field device operating in real-time to control theprocess in the process plant.
 55. The apparatus of claim 38, wherein thecomputer-executable instructions are further executable to store anidentifier of the variation of the behavior of the target processelement and of an indication of the at least one or more sources of thevariation of the behavior of the target process element.
 56. Theapparatus of claim 55, wherein the identifier of the variation of thebehavior of the target process element and of the indication of the atleast one or more sources is provided by a user.